| { |
| "generated_at": "2026-09-17T06:17:24Z", |
| "mode": "DRAFT_EXPLORATION", |
| "publish_eligible": false, |
| "registries": { |
| "architecture": { |
| "criteria": [ |
| { |
| "allowedAnswers": [ |
| "unknown", |
| "yes", |
| "no" |
| ], |
| "core": true, |
| "id": "evaluation_ready", |
| "question": "Is there a representative evaluation set and an explicit acceptance decision?" |
| }, |
| { |
| "allowedAnswers": [ |
| "unknown", |
| "yes", |
| "no" |
| ], |
| "core": true, |
| "id": "fixed_steps", |
| "question": "Can the work be described as a stable sequence of steps?" |
| }, |
| { |
| "allowedAnswers": [ |
| "unknown", |
| "yes", |
| "no" |
| ], |
| "core": true, |
| "id": "external_knowledge", |
| "question": "Does the task need a controlled external knowledge source?" |
| }, |
| { |
| "allowedAnswers": [ |
| "unknown", |
| "yes", |
| "no" |
| ], |
| "core": true, |
| "id": "tool_actions", |
| "question": "Must the system perform external tool actions?" |
| }, |
| { |
| "allowedAnswers": [ |
| "unknown", |
| "yes", |
| "no" |
| ], |
| "core": true, |
| "id": "independent_subtasks", |
| "question": "Can subtasks be isolated with explicit input and output contracts?" |
| }, |
| { |
| "allowedAnswers": [ |
| "unknown", |
| "yes", |
| "no" |
| ], |
| "core": true, |
| "id": "parallel_justified", |
| "question": "Does a measured baseline justify the added coordination cost?" |
| }, |
| { |
| "allowedAnswers": [ |
| "unknown", |
| "yes", |
| "no" |
| ], |
| "core": false, |
| "id": "approval_boundary", |
| "question": "For tool actions, are authorization and human approval boundaries explicit?" |
| }, |
| { |
| "allowedAnswers": [ |
| "unknown", |
| "yes", |
| "no" |
| ], |
| "core": false, |
| "id": "recovery_tested", |
| "question": "For tool actions, has failure recovery been exercised?" |
| } |
| ], |
| "limitations": [ |
| "Original engineering proposal, not an empirical benchmark or a released canonical source.", |
| "Patterns may be combined after evaluation; the first match is a starting point, not proof of fitness.", |
| "The 2024 pattern reference is conceptual; current framework features must be verified separately." |
| ], |
| "method": "Ascending priority, first matching rule; all AND any AND none; unspecified answers are unknown.", |
| "references": [ |
| { |
| "checked_at": "2026-09-17", |
| "scope": "Conceptual distinction between fixed workflows and dynamic agents, and common composition patterns; not validation of these proposed rules.", |
| "url": "https://www.anthropic.com/engineering/building-effective-agents" |
| } |
| ], |
| "rules": [ |
| { |
| "id": "ARCH-NO-EVALUATION", |
| "next_steps": [ |
| "Define representative cases, failure criteria and an accountable acceptance owner before selecting an autonomous path." |
| ], |
| "outcome": "EVALUATION_REQUIRED", |
| "priority": 10, |
| "rejected_options": [ |
| "AUTONOMOUS_RELEASE" |
| ], |
| "when": { |
| "all": [ |
| { |
| "criterionId": "evaluation_ready", |
| "operator": "equals", |
| "value": "no" |
| } |
| ], |
| "any": [], |
| "none": [] |
| } |
| }, |
| { |
| "id": "ARCH-UNKNOWN-CORE", |
| "next_steps": [ |
| "Resolve the unknown core questions; unknown is not equivalent to no." |
| ], |
| "outcome": "INSUFFICIENT_EVIDENCE", |
| "priority": 20, |
| "rejected_options": [], |
| "when": { |
| "all": [], |
| "any": [ |
| { |
| "criterionId": "evaluation_ready", |
| "operator": "equals", |
| "value": "unknown" |
| }, |
| { |
| "criterionId": "fixed_steps", |
| "operator": "equals", |
| "value": "unknown" |
| }, |
| { |
| "criterionId": "external_knowledge", |
| "operator": "equals", |
| "value": "unknown" |
| }, |
| { |
| "criterionId": "tool_actions", |
| "operator": "equals", |
| "value": "unknown" |
| }, |
| { |
| "criterionId": "independent_subtasks", |
| "operator": "equals", |
| "value": "unknown" |
| }, |
| { |
| "criterionId": "parallel_justified", |
| "operator": "equals", |
| "value": "unknown" |
| } |
| ], |
| "none": [] |
| } |
| }, |
| { |
| "id": "ARCH-ACTION-BOUNDARY", |
| "next_steps": [ |
| "Define scoped authorization, approval, containment and recovery for external effects." |
| ], |
| "outcome": "ACTION_BOUNDARY_REQUIRED", |
| "priority": 30, |
| "rejected_options": [ |
| "AUTONOMOUS_TOOL_ACTIONS" |
| ], |
| "when": { |
| "all": [ |
| { |
| "criterionId": "tool_actions", |
| "operator": "equals", |
| "value": "yes" |
| } |
| ], |
| "any": [ |
| { |
| "criterionId": "approval_boundary", |
| "operator": "not_equals", |
| "value": "yes" |
| }, |
| { |
| "criterionId": "recovery_tested", |
| "operator": "not_equals", |
| "value": "yes" |
| } |
| ], |
| "none": [] |
| } |
| }, |
| { |
| "id": "ARCH-FIXED-WORKFLOW", |
| "next_steps": [ |
| "Implement explicit step transitions and validation gates; attach retrieval or approved tools only where the step requires them." |
| ], |
| "outcome": "DETERMINISTIC_WORKFLOW", |
| "priority": 40, |
| "rejected_options": [], |
| "when": { |
| "all": [ |
| { |
| "criterionId": "fixed_steps", |
| "operator": "equals", |
| "value": "yes" |
| } |
| ], |
| "any": [], |
| "none": [] |
| } |
| }, |
| { |
| "id": "ARCH-RETRIEVAL", |
| "next_steps": [ |
| "Validate source access, retrieval coverage and answer grounding against the evaluation set." |
| ], |
| "outcome": "RETRIEVAL_PIPELINE", |
| "priority": 50, |
| "rejected_options": [], |
| "when": { |
| "all": [ |
| { |
| "criterionId": "tool_actions", |
| "operator": "equals", |
| "value": "no" |
| }, |
| { |
| "criterionId": "external_knowledge", |
| "operator": "equals", |
| "value": "yes" |
| } |
| ], |
| "any": [], |
| "none": [] |
| } |
| }, |
| { |
| "id": "ARCH-ORCHESTRATION", |
| "next_steps": [ |
| "Define worker boundaries, a coordinator merge contract, cancellation and duplicate-effect handling before an evaluation." |
| ], |
| "outcome": "ORCHESTRATOR_WORKERS", |
| "priority": 60, |
| "rejected_options": [], |
| "when": { |
| "all": [ |
| { |
| "criterionId": "independent_subtasks", |
| "operator": "equals", |
| "value": "yes" |
| }, |
| { |
| "criterionId": "parallel_justified", |
| "operator": "equals", |
| "value": "yes" |
| } |
| ], |
| "any": [], |
| "none": [] |
| } |
| }, |
| { |
| "id": "ARCH-BOUNDED-AGENT", |
| "next_steps": [ |
| "Start with one agent and scoped tools, iteration limits and a tested stop path; compare it with the simplest working baseline." |
| ], |
| "outcome": "BOUNDED_TOOL_AGENT", |
| "priority": 70, |
| "rejected_options": [], |
| "when": { |
| "all": [ |
| { |
| "criterionId": "tool_actions", |
| "operator": "equals", |
| "value": "yes" |
| } |
| ], |
| "any": [], |
| "none": [] |
| } |
| }, |
| { |
| "id": "ARCH-BASELINE", |
| "next_steps": [ |
| "Evaluate a single-call baseline before adding dynamic coordination." |
| ], |
| "outcome": "SINGLE_CALL_BASELINE", |
| "priority": 80, |
| "rejected_options": [], |
| "when": { |
| "all": [ |
| { |
| "criterionId": "evaluation_ready", |
| "operator": "equals", |
| "value": "yes" |
| } |
| ], |
| "any": [], |
| "none": [] |
| } |
| } |
| ], |
| "version": "0.1.0-draft.1" |
| }, |
| "build_buy": { |
| "claims": { |
| "claims": [ |
| { |
| "boundary": "This is an authored model contract, not an externally calibrated probability, forecast or benchmark.", |
| "id": "CLM-METHOD-001", |
| "sourceIds": [ |
| "SRC-PHAROS-BUILD-BUY" |
| ], |
| "statement": "The scorecard uses 12 criteria with total weight 31, a signed decision index from -100 to +100 and transparent thresholds at -30 and +30." |
| }, |
| { |
| "boundary": "NIST guidance is voluntary and does not transfer legal or operational accountability.", |
| "id": "CLM-RISK-001", |
| "sourceIds": [ |
| "SRC-NIST-AI-RMF", |
| "SRC-NIST-GAI-PROFILE" |
| ], |
| "statement": "AI sourcing decisions still require full-lifecycle risk ownership across design, development, use, evaluation and acquisition." |
| }, |
| { |
| "boundary": "The categories are broad; each organization must supply its own amounts and measurable workload units.", |
| "id": "CLM-TCO-001", |
| "sourceIds": [ |
| "SRC-FINOPS-TCO" |
| ], |
| "statement": "Technology TCO should include acquisition, management, support, labor, downtime, training and other productivity costs across time." |
| }, |
| { |
| "boundary": "The cited consultancy framework is a relevance source, not independent proof of which path will succeed.", |
| "id": "CLM-READINESS-001", |
| "sourceIds": [ |
| "SRC-KPMG-BUILD-BUY-BORROW" |
| ], |
| "statement": "Workforce capability, infrastructure, context and data, security, regulatory requirements and ability to scale are relevant to an agent sourcing decision." |
| }, |
| { |
| "boundary": "The paper is conceptual and does not empirically validate this scorecard's weights or thresholds.", |
| "id": "CLM-FACTOR-001", |
| "sourceIds": [ |
| "SRC-ARXIV-MAKE-BUY" |
| ], |
| "statement": "Cost, strategic differentiation, asset specificity, vendor lock-in, time to market, quality and compliance, and organizational capability are recurring make-or-buy determinants in the cited conceptual analysis." |
| } |
| ], |
| "version": "1.0.0" |
| }, |
| "criteria": { |
| "criteria": [ |
| { |
| "gateCritical": false, |
| "id": "strategic_differentiation", |
| "label": "Strategic differentiation", |
| "options": [ |
| { |
| "blocks": [], |
| "evidencePrompt": "Name the available products and the exact capability each already covers.", |
| "id": "buy", |
| "interpretation": "The workflow is available from several credible products and is not central to competitive advantage.", |
| "label": "Useful but commodity", |
| "value": -1 |
| }, |
| { |
| "blocks": [], |
| "evidencePrompt": "Separate commodity platform functions from logic that must remain yours.", |
| "id": "hybrid", |
| "interpretation": "The shell is common, but domain rules or user experience contain differentiated work.", |
| "label": "Some differentiated logic", |
| "value": 0 |
| }, |
| { |
| "blocks": [], |
| "evidencePrompt": "Describe what a competitor could not reproduce by buying the same platform.", |
| "id": "build", |
| "interpretation": "The agent's behavior, feedback loop or workflow is part of the product moat.", |
| "label": "Core proprietary capability", |
| "value": 1 |
| }, |
| { |
| "blocks": [], |
| "evidencePrompt": "Get a product-owner decision before treating the score as ready.", |
| "id": "unknown", |
| "interpretation": "The strategic role has not been agreed.", |
| "label": "Unknown", |
| "value": null |
| } |
| ], |
| "order": 1, |
| "question": "How much of the agent's behavior creates a durable product or operating advantage?", |
| "weight": 3, |
| "whyItMatters": "Commodity capability is expensive to recreate. Differentiating decision logic can be risky to surrender to a vendor roadmap." |
| }, |
| { |
| "gateCritical": false, |
| "id": "workflow_uniqueness", |
| "label": "Workflow and policy uniqueness", |
| "options": [ |
| { |
| "blocks": [], |
| "evidencePrompt": "List the required steps and map them to available vendor features.", |
| "id": "buy", |
| "interpretation": "The process follows common product patterns with limited exceptions.", |
| "label": "Standard workflow", |
| "value": -1 |
| }, |
| { |
| "blocks": [], |
| "evidencePrompt": "Mark each step as configure, extend or own.", |
| "id": "hybrid", |
| "interpretation": "Most steps are standard while a few rules or actions need owned extensions.", |
| "label": "Standard core, custom edges", |
| "value": 0 |
| }, |
| { |
| "blocks": [], |
| "evidencePrompt": "Document the exceptions that generic products cannot express safely.", |
| "id": "build", |
| "interpretation": "The process depends on organization-specific rules, exceptions or sequencing.", |
| "label": "Proprietary policy engine", |
| "value": 1 |
| }, |
| { |
| "blocks": [], |
| "evidencePrompt": "Run a workflow discovery session with operators and control owners.", |
| "id": "unknown", |
| "interpretation": "The real workflow has not been mapped.", |
| "label": "Unknown", |
| "value": null |
| } |
| ], |
| "order": 2, |
| "question": "Are the agent's rules, tools and exception paths standard or organization-specific?", |
| "weight": 3, |
| "whyItMatters": "A standard workflow favors configuration. Deep policy logic and unusual exceptions increase the value of owned orchestration." |
| }, |
| { |
| "gateCritical": true, |
| "id": "data_control", |
| "label": "Data control and residency", |
| "options": [ |
| { |
| "blocks": [], |
| "evidencePrompt": "Attach the approved data-flow, retention terms and deletion test.", |
| "id": "buy", |
| "interpretation": "A shortlisted vendor can meet documented handling and residency requirements.", |
| "label": "Approved vendor path exists", |
| "value": -1 |
| }, |
| { |
| "blocks": [], |
| "evidencePrompt": "Draw the trust boundary and identify which fields may cross it.", |
| "id": "hybrid", |
| "interpretation": "Commodity services are acceptable outside a controlled data and policy boundary.", |
| "label": "Sensitive boundary can be isolated", |
| "value": 0 |
| }, |
| { |
| "blocks": [ |
| "BUY" |
| ], |
| "evidencePrompt": "Cite the binding policy, regulation or security decision and the failed vendor requirement.", |
| "id": "build", |
| "interpretation": "No approved vendor route currently meets a mandatory data-control constraint.", |
| "label": "Controlled custom path required", |
| "value": 1 |
| }, |
| { |
| "blocks": [], |
| "evidencePrompt": "Complete data classification and a vendor data-flow review.", |
| "id": "unknown", |
| "interpretation": "Data classification or vendor handling is unresolved.", |
| "label": "Unknown", |
| "value": null |
| } |
| ], |
| "order": 3, |
| "question": "Can an approved vendor path meet data location, retention, deletion and training-use constraints?", |
| "weight": 3, |
| "whyItMatters": "A mandatory control that the shortlist cannot meet disqualifies a pure buy decision regardless of feature fit." |
| }, |
| { |
| "gateCritical": false, |
| "id": "integration_depth", |
| "label": "Integration depth", |
| "options": [ |
| { |
| "blocks": [], |
| "evidencePrompt": "Verify the exact objects, permissions and rate limits in the vendor connector.", |
| "id": "buy", |
| "interpretation": "The use case relies on supported connectors and limited read-oriented access.", |
| "label": "Standard connectors and reads", |
| "value": -1 |
| }, |
| { |
| "blocks": [], |
| "evidencePrompt": "Identify the crown-jewel integrations and keep their contracts portable.", |
| "id": "hybrid", |
| "interpretation": "Common systems are covered, but one or more integrations require owned adapters.", |
| "label": "Mixed connector depth", |
| "value": 0 |
| }, |
| { |
| "blocks": [], |
| "evidencePrompt": "Map identity, authorization, idempotency, rollback and reconciliation for each action.", |
| "id": "build", |
| "interpretation": "The agent needs legacy access, custom APIs, transactional writes or organization-specific recovery.", |
| "label": "Deep custom transaction paths", |
| "value": 1 |
| }, |
| { |
| "blocks": [], |
| "evidencePrompt": "Inventory systems, actions, permissions, error modes and data owners.", |
| "id": "unknown", |
| "interpretation": "The integration inventory is incomplete.", |
| "label": "Unknown", |
| "value": null |
| } |
| ], |
| "order": 4, |
| "question": "How deeply must the agent interact with systems, permissions and failure recovery?", |
| "weight": 3, |
| "whyItMatters": "Connector count alone is weak evidence. Permission fidelity, write actions and recovery semantics drive integration ownership." |
| }, |
| { |
| "gateCritical": false, |
| "id": "time_to_value", |
| "label": "Time to validated value", |
| "options": [ |
| { |
| "blocks": [], |
| "evidencePrompt": "Name the decision deadline and the KPI that must be measured by then.", |
| "id": "buy", |
| "interpretation": "The business needs a production-like baseline before a custom platform could be staffed and built.", |
| "label": "Evidence needed immediately", |
| "value": -1 |
| }, |
| { |
| "blocks": [], |
| "evidencePrompt": "Define the first reversible slice and the later migration gate.", |
| "id": "hybrid", |
| "interpretation": "A vendor or open component can establish a baseline while custom boundaries are validated.", |
| "label": "Phased evidence window", |
| "value": 0 |
| }, |
| { |
| "blocks": [], |
| "evidencePrompt": "Confirm staffing dates, release gates and the cost of delayed learning.", |
| "id": "build", |
| "interpretation": "The roadmap can absorb iterative architecture, evaluation and operating-model work.", |
| "label": "Longer validation horizon", |
| "value": 1 |
| }, |
| { |
| "blocks": [], |
| "evidencePrompt": "Set a business deadline and a production-like validation target.", |
| "id": "unknown", |
| "interpretation": "No decision deadline or measurable outcome is agreed.", |
| "label": "Unknown", |
| "value": null |
| } |
| ], |
| "order": 5, |
| "question": "How soon must a production-like workflow produce measurable evidence?", |
| "weight": 3, |
| "whyItMatters": "Urgency favors a configurable baseline, while a longer decision window can support owned architecture and repeated evaluation." |
| }, |
| { |
| "gateCritical": true, |
| "id": "team_capability", |
| "label": "Engineering and product capability", |
| "options": [ |
| { |
| "blocks": [ |
| "BUILD" |
| ], |
| "evidencePrompt": "Record the unfilled roles and the opportunity cost of reallocating staff.", |
| "id": "buy", |
| "interpretation": "The organization cannot assign a durable cross-functional owner team.", |
| "label": "No dedicated build team", |
| "value": -1 |
| }, |
| { |
| "blocks": [], |
| "evidencePrompt": "Assign owners for product, platform, data, security and evaluation.", |
| "id": "hybrid", |
| "interpretation": "Internal engineers can own integration and domain logic while buying missing platform capability.", |
| "label": "Strong software team, selective gaps", |
| "value": 0 |
| }, |
| { |
| "blocks": [], |
| "evidencePrompt": "Name the people, allocation, prior systems and on-call plan.", |
| "id": "build", |
| "interpretation": "A durable team has relevant delivery and operating experience.", |
| "label": "Experienced agent delivery team", |
| "value": 1 |
| }, |
| { |
| "blocks": [], |
| "evidencePrompt": "Replace role names with named owners and committed capacity.", |
| "id": "unknown", |
| "interpretation": "Staffing is hypothetical.", |
| "label": "Unknown", |
| "value": null |
| } |
| ], |
| "order": 6, |
| "question": "Can a named internal team design, test and evolve the agent beyond a prototype?", |
| "weight": 3, |
| "whyItMatters": "Build capacity includes product ownership, data engineering, security and evaluation, not only prompt or application code." |
| }, |
| { |
| "gateCritical": true, |
| "id": "lifecycle_capacity", |
| "label": "Lifecycle ownership", |
| "options": [ |
| { |
| "blocks": [ |
| "BUILD" |
| ], |
| "evidencePrompt": "Confirm what the vendor operates and what remains your responsibility.", |
| "id": "buy", |
| "interpretation": "The organization needs a vendor to carry much of the platform lifecycle.", |
| "label": "Need managed lifecycle coverage", |
| "value": -1 |
| }, |
| { |
| "blocks": [], |
| "evidencePrompt": "Create a RACI for monitoring, changes, incidents, data and access.", |
| "id": "hybrid", |
| "interpretation": "The vendor runs commodity layers while the organization owns domain quality and incidents.", |
| "label": "Shared operating model", |
| "value": 0 |
| }, |
| { |
| "blocks": [], |
| "evidencePrompt": "Attach the evaluation cadence, on-call rota, rollback path and change policy.", |
| "id": "build", |
| "interpretation": "The organization can own releases, regressions, infrastructure and incident response.", |
| "label": "Full internal lifecycle team", |
| "value": 1 |
| }, |
| { |
| "blocks": [], |
| "evidencePrompt": "Assign durable owners before approving a build.", |
| "id": "unknown", |
| "interpretation": "No operating owner has accepted responsibility.", |
| "label": "Unknown", |
| "value": null |
| } |
| ], |
| "order": 7, |
| "question": "Who will operate evaluation, monitoring, incidents and model or vendor changes?", |
| "weight": 3, |
| "whyItMatters": "The sourcing choice changes the work, but it never removes responsibility for outcomes and controls." |
| }, |
| { |
| "gateCritical": false, |
| "id": "capital_shape", |
| "label": "Budget and capital shape", |
| "options": [ |
| { |
| "blocks": [], |
| "evidencePrompt": "Record approved setup spend, recurring spend and procurement limits.", |
| "id": "buy", |
| "interpretation": "Subscription or usage spend is easier to approve than a larger internal build.", |
| "label": "Lower initial commitment", |
| "value": -1 |
| }, |
| { |
| "blocks": [], |
| "evidencePrompt": "Separate vendor, integration, internal labor and contingency budgets.", |
| "id": "hybrid", |
| "interpretation": "The budget can fund a platform and a bounded custom layer.", |
| "label": "Balanced spend profile", |
| "value": 0 |
| }, |
| { |
| "blocks": [], |
| "evidencePrompt": "Verify capital, headcount and contingency approvals.", |
| "id": "build", |
| "interpretation": "The organization can fund discovery, engineering and operating capability before benefits stabilize.", |
| "label": "Front-loaded investment is approved", |
| "value": 1 |
| }, |
| { |
| "blocks": [], |
| "evidencePrompt": "Get separate approval ranges for one-time and recurring spend.", |
| "id": "unknown", |
| "interpretation": "Budget shape is not approved.", |
| "label": "Unknown", |
| "value": null |
| } |
| ], |
| "order": 8, |
| "question": "Does the organization prefer lower initial commitment or controlled long-term investment?", |
| "weight": 2, |
| "whyItMatters": "The same nominal budget can produce different decisions depending on cash timing and approved headcount." |
| }, |
| { |
| "gateCritical": false, |
| "id": "scale_economics", |
| "label": "Scale and unit economics", |
| "options": [ |
| { |
| "blocks": [], |
| "evidencePrompt": "Define the unit of work and a bounded adoption range.", |
| "id": "buy", |
| "interpretation": "The organization benefits from avoiding fixed platform investment while demand is unproven.", |
| "label": "Demand is uncertain or modest", |
| "value": -1 |
| }, |
| { |
| "blocks": [], |
| "evidencePrompt": "Track cost per successful task, active user, action and exception.", |
| "id": "hybrid", |
| "interpretation": "Usage can be measured, but the economic crossover requires a live baseline.", |
| "label": "Scale is measurable, crossover unclear", |
| "value": 0 |
| }, |
| { |
| "blocks": [], |
| "evidencePrompt": "Show demand, peak shape, growth assumptions and cost per successful outcome.", |
| "id": "build", |
| "interpretation": "Stable high volume can justify owned cost controls and reusable platform work.", |
| "label": "High predictable scale", |
| "value": 1 |
| }, |
| { |
| "blocks": [], |
| "evidencePrompt": "Define a measurable unit and collect a baseline.", |
| "id": "unknown", |
| "interpretation": "No unit or demand model exists.", |
| "label": "Unknown", |
| "value": null |
| } |
| ], |
| "order": 9, |
| "question": "Are demand, workload units and marginal costs measurable enough to compare paths?", |
| "weight": 2, |
| "whyItMatters": "A low pilot price says little about per-user, per-task or per-action economics at production scale." |
| }, |
| { |
| "gateCritical": false, |
| "id": "portability_tolerance", |
| "label": "Portability and vendor dependency", |
| "options": [ |
| { |
| "blocks": [], |
| "evidencePrompt": "Record renewal, export, termination assistance and deletion terms.", |
| "id": "buy", |
| "interpretation": "The organization accepts a managed platform dependency with bounded exit terms.", |
| "label": "Contractual dependency is acceptable", |
| "value": -1 |
| }, |
| { |
| "blocks": [], |
| "evidencePrompt": "Run an export and redeploy test for the assets that must remain yours.", |
| "id": "hybrid", |
| "interpretation": "Domain logic, data and evals remain portable while replaceable services are bought.", |
| "label": "Portable core, managed shell", |
| "value": 0 |
| }, |
| { |
| "blocks": [], |
| "evidencePrompt": "List every asset needed to operate after vendor account termination.", |
| "id": "build", |
| "interpretation": "The organization needs control over runtime boundaries and migration paths.", |
| "label": "High portability requirement", |
| "value": 1 |
| }, |
| { |
| "blocks": [], |
| "evidencePrompt": "Write an exit scenario before vendor selection or platform build.", |
| "id": "unknown", |
| "interpretation": "Exit requirements are undefined.", |
| "label": "Unknown", |
| "value": null |
| } |
| ], |
| "order": 10, |
| "question": "What must remain movable if the model, platform or commercial terms change?", |
| "weight": 2, |
| "whyItMatters": "Code ownership is incomplete when prompts, evals, state, logs or deployment cannot leave the vendor runtime." |
| }, |
| { |
| "gateCritical": false, |
| "id": "evaluation_control", |
| "label": "Evaluation and observability control", |
| "options": [ |
| { |
| "blocks": [], |
| "evidencePrompt": "Verify each required artifact in a production-like trial.", |
| "id": "buy", |
| "interpretation": "The platform exposes the required traces, eval hooks, exports and release controls.", |
| "label": "Vendor evidence is sufficient", |
| "value": -1 |
| }, |
| { |
| "blocks": [], |
| "evidencePrompt": "Keep test data, metrics and release criteria outside the vendor-only surface.", |
| "id": "hybrid", |
| "interpretation": "The platform can be wrapped by an organization-owned test and observability layer.", |
| "label": "Own evals around a bought runtime", |
| "value": 0 |
| }, |
| { |
| "blocks": [], |
| "evidencePrompt": "Name the failure taxonomy, test set, trace fields and stop conditions.", |
| "id": "build", |
| "interpretation": "The use case needs custom evaluation, traces or action-level controls unavailable from products.", |
| "label": "Domain-specific evidence is central", |
| "value": 1 |
| }, |
| { |
| "blocks": [], |
| "evidencePrompt": "Define what must be observable before comparing vendors or architectures.", |
| "id": "unknown", |
| "interpretation": "The evidence contract is not defined.", |
| "label": "Unknown", |
| "value": null |
| } |
| ], |
| "order": 11, |
| "question": "Can a bought product expose the evidence needed to test domain quality and actions?", |
| "weight": 2, |
| "whyItMatters": "A system cannot be governed from a demo score. Teams need test sets, traces, failure labels and release gates tied to their own workflow." |
| }, |
| { |
| "gateCritical": true, |
| "id": "compliance_path", |
| "label": "Compliance and third-party path", |
| "options": [ |
| { |
| "blocks": [], |
| "evidencePrompt": "Attach the approved control matrix, contracts and residual-risk owner.", |
| "id": "buy", |
| "interpretation": "A vendor route can satisfy documented procurement, security and compliance gates.", |
| "label": "Approved vendor controls fit", |
| "value": -1 |
| }, |
| { |
| "blocks": [], |
| "evidencePrompt": "Map each obligation to the vendor, internal owner or shared process.", |
| "id": "hybrid", |
| "interpretation": "The path needs vendor evidence plus organization-owned controls and records.", |
| "label": "Control boundary must be shared", |
| "value": 0 |
| }, |
| { |
| "blocks": [ |
| "BUY" |
| ], |
| "evidencePrompt": "Cite the failed gate and the evidence a controlled build must produce.", |
| "id": "build", |
| "interpretation": "A mandatory approval or control cannot be met by the current vendor shortlist.", |
| "label": "Custom controlled path required", |
| "value": 1 |
| }, |
| { |
| "blocks": [], |
| "evidencePrompt": "Get legal, security and risk owners to classify the use case.", |
| "id": "unknown", |
| "interpretation": "The applicable obligations or approval route are unresolved.", |
| "label": "Unknown", |
| "value": null |
| } |
| ], |
| "order": 12, |
| "question": "Which sourcing path can produce the required approvals, records and accountability?", |
| "weight": 2, |
| "whyItMatters": "A vendor certification does not transfer accountability. A custom build also creates documentation and control obligations." |
| } |
| ], |
| "publishedOn": "2026-08-25", |
| "thresholds": { |
| "boundaryMargin": 7, |
| "buildMin": 30, |
| "buyMax": -30, |
| "minimumKnownWeightShare": 0.75 |
| }, |
| "totalWeight": 31, |
| "version": "1.0.0" |
| }, |
| "methodology": { |
| "boundaries": [ |
| "The decision index is an authored decision-support model, not a probability, benchmark, forecast or confidence score.", |
| "Thresholds are transparent editorial defaults and have not been statistically calibrated against project outcomes.", |
| "TCO values are editable scenario assumptions, not Pharos pricing and not market averages.", |
| "A low-cost path can still fail a mandatory data, security, legal or operating constraint.", |
| "The tool does not replace architecture discovery, procurement, security review, legal advice or financial analysis." |
| ], |
| "exportSchemaVersion": "1.0.0", |
| "formula": "round-half-away-from-zero(100 * signed known points / known weight)", |
| "lastVerifiedOn": "2026-08-25", |
| "outcomeIds": [ |
| "BUY", |
| "HYBRID", |
| "BUILD", |
| "UNRESOLVED" |
| ], |
| "procedure": [ |
| "Choose one evidence-backed answer for each criterion.", |
| "Multiply each known answer value by its criterion weight and sum signed points.", |
| "Normalize signed points only across known weight so unknown answers never act as neutral evidence.", |
| "Apply BUY at -30 or lower, BUILD at +30 or higher and HYBRID between those thresholds.", |
| "Withhold a decision when known-weight coverage is below 75 percent or any gate-critical criterion is unknown.", |
| "Show a decision-boundary warning when the index is within seven points of either outcome threshold.", |
| "If a selected constraint blocks the numeric outcome, require constraint review and return HYBRID as the working architecture boundary.", |
| "Compare the result with editable three-year TCO assumptions and record both artifacts together." |
| ], |
| "publishedOn": "2026-08-25", |
| "registryVersions": { |
| "claims": "1.0.0", |
| "criteria": "1.0.0", |
| "sources": "1.0.0", |
| "tco": "1.0.0" |
| }, |
| "scoringScale": { |
| "build": 1, |
| "buy": -1, |
| "hybrid": 0, |
| "unknown": null |
| }, |
| "statusIds": [ |
| "DECISION_READY", |
| "INSUFFICIENT_DECISION_EVIDENCE", |
| "CONSTRAINT_REVIEW_REQUIRED" |
| ], |
| "version": "1.0.0" |
| }, |
| "profiles": { |
| "profiles": [ |
| { |
| "answers": { |
| "capital_shape": "unknown", |
| "compliance_path": "unknown", |
| "data_control": "unknown", |
| "evaluation_control": "unknown", |
| "integration_depth": "unknown", |
| "lifecycle_capacity": "unknown", |
| "portability_tolerance": "unknown", |
| "scale_economics": "unknown", |
| "strategic_differentiation": "unknown", |
| "team_capability": "unknown", |
| "time_to_value": "unknown", |
| "workflow_uniqueness": "unknown" |
| }, |
| "description": "Every answer remains unknown until evidence is supplied.", |
| "id": "blank", |
| "label": "Start blank" |
| }, |
| { |
| "answers": { |
| "capital_shape": "buy", |
| "compliance_path": "buy", |
| "data_control": "buy", |
| "evaluation_control": "hybrid", |
| "integration_depth": "buy", |
| "lifecycle_capacity": "buy", |
| "portability_tolerance": "buy", |
| "scale_economics": "buy", |
| "strategic_differentiation": "buy", |
| "team_capability": "hybrid", |
| "time_to_value": "buy", |
| "workflow_uniqueness": "buy" |
| }, |
| "description": "Common workflow, modest scale, approved vendor route and urgent baseline.", |
| "id": "commodity-internal", |
| "label": "Commodity internal workflow" |
| }, |
| { |
| "answers": { |
| "capital_shape": "build", |
| "compliance_path": "build", |
| "data_control": "build", |
| "evaluation_control": "build", |
| "integration_depth": "build", |
| "lifecycle_capacity": "build", |
| "portability_tolerance": "build", |
| "scale_economics": "build", |
| "strategic_differentiation": "build", |
| "team_capability": "build", |
| "time_to_value": "hybrid", |
| "workflow_uniqueness": "build" |
| }, |
| "description": "Proprietary workflow with deep integrations, controlled data path and durable internal ownership.", |
| "id": "controlled-core", |
| "label": "Differentiated controlled system" |
| }, |
| { |
| "answers": { |
| "capital_shape": "hybrid", |
| "compliance_path": "hybrid", |
| "data_control": "hybrid", |
| "evaluation_control": "hybrid", |
| "integration_depth": "build", |
| "lifecycle_capacity": "hybrid", |
| "portability_tolerance": "hybrid", |
| "scale_economics": "hybrid", |
| "strategic_differentiation": "hybrid", |
| "team_capability": "hybrid", |
| "time_to_value": "hybrid", |
| "workflow_uniqueness": "hybrid" |
| }, |
| "description": "Buy commodity infrastructure while retaining domain logic, critical adapters and evaluation assets.", |
| "id": "hybrid-transition", |
| "label": "Hybrid transition" |
| } |
| ], |
| "version": "1.0.0" |
| }, |
| "sources": { |
| "sources": [ |
| { |
| "id": "SRC-PHAROS-BUILD-BUY", |
| "limitations": "Used to seed the topic vocabulary and six-factor starting point. Its engagement examples, universal ranges and commercial conclusions are not treated as independent validation of this tool's thresholds.", |
| "publisher": "Pharos Production", |
| "sourceType": "first-party", |
| "title": "Build vs Buy AI Agent: 2026 Decision Framework", |
| "url": "https://pharosproduction.com/insights/engineering/build-vs-buy-ai-agent/", |
| "verifiedOn": "2026-08-25" |
| }, |
| { |
| "id": "SRC-NIST-AI-RMF", |
| "limitations": "Voluntary risk-management guidance. NIST states that AI RMF 1.0 is being revised; it does not prescribe build, buy or hybrid thresholds.", |
| "publisher": "National Institute of Standards and Technology", |
| "sourceType": "primary-standard", |
| "title": "AI Risk Management Framework", |
| "url": "https://www.nist.gov/itl/ai-risk-management-framework", |
| "verifiedOn": "2026-08-25" |
| }, |
| { |
| "id": "SRC-NIST-GAI-PROFILE", |
| "limitations": "A cross-sectoral companion profile for generative AI risk management. It informs lifecycle and acquisition questions but does not validate economic weights or sourcing outcomes.", |
| "publisher": "National Institute of Standards and Technology", |
| "sourceType": "primary-standard", |
| "title": "Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile", |
| "url": "https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf", |
| "verifiedOn": "2026-08-25" |
| }, |
| { |
| "id": "SRC-FINOPS-TCO", |
| "limitations": "Defines broad TCO and unit-economics concepts for technology costs. It supplies no AI-agent price ranges and does not validate this scenario's illustrative amounts.", |
| "publisher": "FinOps Foundation", |
| "sourceType": "primary-framework", |
| "title": "FinOps Terminology: Total Cost of Ownership and Unit Economics", |
| "url": "https://framework.finops.org/assets/terminology/", |
| "verifiedOn": "2026-08-25" |
| }, |
| { |
| "id": "SRC-KPMG-BUILD-BUY-BORROW", |
| "limitations": "A consultancy-authored framework with commercial interests. It supports the relevance of readiness, infrastructure, data, security, compliance and scale dimensions, not this tool's score formula.", |
| "publisher": "KPMG Ireland", |
| "sourceType": "first-party-research", |
| "title": "Agentic AI Untangled: Navigating the Build, Buy, or Borrow Decision", |
| "url": "https://assets.kpmg.com/content/dam/kpmgsites/ie/pdf/insights/ai-artificial-intelligence/ie-agentic-ai-build-buy-borrow.pdf.coredownload.inline.pdf", |
| "verifiedOn": "2026-08-25" |
| }, |
| { |
| "id": "SRC-ARXIV-MAKE-BUY", |
| "limitations": "A conceptual preprint rather than an empirical calibration study. Its factor taxonomy informs coverage but cannot establish universal sourcing thresholds or expected outcomes.", |
| "publisher": "arXiv", |
| "sourceType": "conceptual-preprint", |
| "title": "The Buy-or-Build Decision, Revisited: How Agentic AI Changes the Economics of Enterprise Software", |
| "url": "https://arxiv.org/abs/2604.26482", |
| "verifiedOn": "2026-08-25" |
| } |
| ], |
| "verifiedOn": "2026-08-25", |
| "version": "1.0.0" |
| }, |
| "tco": { |
| "categories": [ |
| { |
| "cadence": "one_time", |
| "description": "Workflow, data, risk, product and architecture discovery before implementation.", |
| "id": "discovery_design", |
| "label": "Discovery and architecture" |
| }, |
| { |
| "cadence": "one_time", |
| "description": "Configuration or development, integrations, environments, testing and release preparation.", |
| "id": "implementation_integration", |
| "label": "Implementation and integration" |
| }, |
| { |
| "cadence": "one_time", |
| "description": "Data cleanup, permissions, migration, indexing and initial evaluation-set preparation.", |
| "id": "data_migration", |
| "label": "Data preparation and migration" |
| }, |
| { |
| "cadence": "annual", |
| "description": "Platform subscription, model usage, cloud resources, storage, networking and observability services.", |
| "id": "licenses_model_infrastructure", |
| "label": "Licenses, models and infrastructure" |
| }, |
| { |
| "cadence": "annual", |
| "description": "Product ownership, engineering, data, platform operations and incident coverage retained internally.", |
| "id": "internal_operations", |
| "label": "Internal product and operations team" |
| }, |
| { |
| "cadence": "annual", |
| "description": "Test-set maintenance, red teaming, security review, audit evidence and control operation.", |
| "id": "evaluation_security_compliance", |
| "label": "Evaluation, security and compliance" |
| }, |
| { |
| "cadence": "annual", |
| "description": "User enablement, support, workflow change, model or vendor updates and business-process maintenance.", |
| "id": "change_support", |
| "label": "Change, training and support" |
| }, |
| { |
| "cadence": "exit", |
| "description": "Export, migration, contract exit, redeployment, retraining and parallel-run allowance at the horizon.", |
| "id": "exit_switching_reserve", |
| "label": "Exit and switching reserve" |
| } |
| ], |
| "currency": "USD", |
| "defaultScenario": { |
| "boundary": "These figures are not Pharos pricing, vendor quotes or market benchmarks. Replace every amount with organization-specific evidence.", |
| "label": "Editable illustrative scenario", |
| "values": { |
| "build": { |
| "change_support": 60000, |
| "data_migration": 80000, |
| "discovery_design": 90000, |
| "evaluation_security_compliance": 90000, |
| "exit_switching_reserve": 40000, |
| "implementation_integration": 300000, |
| "internal_operations": 260000, |
| "licenses_model_infrastructure": 120000 |
| }, |
| "buy": { |
| "change_support": 50000, |
| "data_migration": 50000, |
| "discovery_design": 40000, |
| "evaluation_security_compliance": 70000, |
| "exit_switching_reserve": 220000, |
| "implementation_integration": 120000, |
| "internal_operations": 100000, |
| "licenses_model_infrastructure": 300000 |
| }, |
| "hybrid": { |
| "change_support": 60000, |
| "data_migration": 60000, |
| "discovery_design": 70000, |
| "evaluation_security_compliance": 80000, |
| "exit_switching_reserve": 130000, |
| "implementation_integration": 210000, |
| "internal_operations": 190000, |
| "licenses_model_infrastructure": 160000 |
| } |
| } |
| }, |
| "horizonYears": 3, |
| "paths": [ |
| "build", |
| "buy", |
| "hybrid" |
| ], |
| "version": "1.0.0" |
| } |
| }, |
| "framework": { |
| "claims": { |
| "claimEvidence": "Named official documentation, repositories, and release records in data/sources.json", |
| "claims": [ |
| { |
| "boundary": "Inclusion is an editorial shortlist boundary, not a universal market ranking.", |
| "disposition": "KEEP", |
| "id": "CLM-001", |
| "role": "PRIMARY", |
| "sourceIds": [ |
| "LG-REL", |
| "MAF-REL", |
| "CR-REL", |
| "OA-REL", |
| "GA-REL" |
| ], |
| "statement": "The comparison contains five maintained, permissively licensed code-first frameworks with documented paths for state, approval, observability, recovery, and deployment.", |
| "status": "OBSERVED" |
| }, |
| { |
| "boundary": "Production durability depends on a persistent checkpointer and replay-safe node design.", |
| "disposition": "KEEP", |
| "id": "CLM-002", |
| "role": "PRIMARY", |
| "sourceIds": [ |
| "LG-PERSIST", |
| "LG-HITL", |
| "LG-SERVER" |
| ], |
| "statement": "LangGraph provides native checkpointed state, interrupts, and durable execution primitives for explicit state-graph workflows.", |
| "status": "OBSERVED" |
| }, |
| { |
| "boundary": "Language and UI integration parity must be verified for the selected client surface.", |
| "disposition": "KEEP", |
| "id": "CLM-003", |
| "role": "PRIMARY", |
| "sourceIds": [ |
| "MAF-CHECKPOINT", |
| "MAF-HITL" |
| ], |
| "statement": "Microsoft Agent Framework workflows provide checkpoint managers and request-based human approval with pending requests preserved across restore.", |
| "status": "OBSERVED" |
| }, |
| { |
| "boundary": "Managed AMP capabilities are distinct from the open-source Python runtime.", |
| "disposition": "KEEP", |
| "id": "CLM-004", |
| "role": "PRIMARY", |
| "sourceIds": [ |
| "CR-FLOWS", |
| "CR-AMP" |
| ], |
| "statement": "CrewAI Flows provide persisted state and human-feedback routing, while CrewAI AMP adds a managed deployment and observability path.", |
| "status": "OBSERVED" |
| }, |
| { |
| "boundary": "The Python SDK does not itself operate an external durable workflow runtime.", |
| "disposition": "KEEP", |
| "id": "CLM-005", |
| "role": "PRIMARY", |
| "sourceIds": [ |
| "OA-RUN", |
| "OA-HITL", |
| "OA-TRACE" |
| ], |
| "statement": "OpenAI Agents SDK provides sessions, serialized approval state, and built-in tracing; process-restart durability uses documented external workflow integrations.", |
| "status": "OBSERVED" |
| }, |
| { |
| "boundary": "In-memory sessions are non-durable and cross-language feature parity is not assumed.", |
| "disposition": "KEEP", |
| "id": "CLM-006", |
| "role": "PRIMARY", |
| "sourceIds": [ |
| "GA-STATE", |
| "GA-HITL", |
| "GA-RESUME", |
| "GA-OBS", |
| "GA-DEPLOY" |
| ], |
| "statement": "Google ADK persists state through durable session services, supports human input and resume in workflows, exports telemetry, and documents several Google Cloud deployment targets.", |
| "status": "OBSERVED" |
| }, |
| { |
| "boundary": "This is a method conclusion drawn from the documented mechanism boundaries, not a vendor performance claim.", |
| "disposition": "KEEP", |
| "id": "CLM-007", |
| "role": "SUPPORTING", |
| "sourceIds": [ |
| "LG-HITL", |
| "MAF-HITL", |
| "CR-FLOWS", |
| "OA-HITL", |
| "GA-HITL" |
| ], |
| "statement": "A framework primitive is not by itself an enterprise control: identity, authorization, policy, audit retention, side-effect idempotency, and incident procedures remain application and operations work.", |
| "status": "OBSERVED" |
| } |
| ], |
| "evidenceCutoff": "2026-08-28", |
| "schemaVersion": "1.0.0" |
| }, |
| "frameworks": { |
| "comparisonBoundary": "Maintained, permissively licensed, code-first agent frameworks with documented state, human-approval, observability, recovery, and deployment paths for enterprise workflows.", |
| "evidenceCutoff": "2026-08-28", |
| "frameworks": [ |
| { |
| "bestFor": "Long-running or failure-sensitive workflows where teams need explicit control over state transitions, pause/resume behavior, and replay boundaries.", |
| "capabilities": { |
| "approval": { |
| "detail": "The interrupt primitive pauses execution, exposes approval context, and resumes the same thread after an external decision.", |
| "sourceIds": [ |
| "LG-HITL" |
| ], |
| "status": "native" |
| }, |
| "deployment": { |
| "detail": "The open-source runtime can run in application infrastructure, while Agent Server packages graphs with persistence and a durable task queue for self-hosted or managed deployment.", |
| "sourceIds": [ |
| "LG-SERVER" |
| ], |
| "status": "self-host-or-managed" |
| }, |
| "observability": { |
| "detail": "Execution paths and state transitions can be traced through LangSmith; tracing can be disabled or conditioned for data-sensitive runs.", |
| "sourceIds": [ |
| "LG-OVERVIEW", |
| "LG-PRIVACY" |
| ], |
| "status": "integrated" |
| }, |
| "recovery": { |
| "detail": "Durable execution resumes from checkpoints after interruption or worker failure; node boundaries determine how much work may repeat.", |
| "sourceIds": [ |
| "LG-PERSIST", |
| "LG-SERVER" |
| ], |
| "status": "native" |
| }, |
| "state": { |
| "detail": "A checkpointer stores graph state as snapshots organized into threads at graph steps; persistent backends replace InMemorySaver for production.", |
| "sourceIds": [ |
| "LG-PERSIST" |
| ], |
| "status": "native" |
| } |
| }, |
| "constraints": "The graph model exposes control but increases design and testing work. Production persistence needs a durable checkpointer or Agent Server rather than the in-memory example backend.", |
| "deploymentControl": [ |
| "self-host", |
| "managed" |
| ], |
| "disqualifiers": [ |
| "A short, deterministic tool call sequence is simpler in ordinary application code", |
| "The team cannot own explicit graph topology and checkpoint migration" |
| ], |
| "id": "langgraph", |
| "languages": [ |
| "python", |
| "typescript" |
| ], |
| "license": "MIT", |
| "modelStrategy": [ |
| "agnostic" |
| ], |
| "name": "LangGraph", |
| "orchestrationModel": "Explicit state graph with nodes, edges, conditional routing, cycles, and checkpointed execution.", |
| "priorities": [ |
| "durable-recovery", |
| "approval", |
| "observability", |
| "explicit-control" |
| ], |
| "release": { |
| "publishedAt": "2026-08-11", |
| "sourceId": "LG-REL", |
| "track": "Python core", |
| "version": "1.2.11" |
| }, |
| "tradeoffs": [ |
| "High control and inspectable state", |
| "More orchestration code and a steeper graph mental model", |
| "LangSmith is an integrated product, not a requirement of the open-source runtime" |
| ] |
| }, |
| { |
| "bestFor": "Enterprises standardizing on .NET, Python, Go, Azure identity, or Microsoft operations while retaining explicit multi-agent workflow control.", |
| "capabilities": { |
| "approval": { |
| "detail": "RequestPort and tool-approval requests pause workflows; pending requests are included in checkpoints and re-emitted after restore.", |
| "sourceIds": [ |
| "MAF-HITL" |
| ], |
| "status": "native" |
| }, |
| "deployment": { |
| "detail": "Agents and workflows run in-process or behind application services and integrate with Microsoft and Azure agent services; deployment architecture remains an application decision.", |
| "sourceIds": [ |
| "MAF-OVERVIEW" |
| ], |
| "status": "self-host-or-managed" |
| }, |
| "observability": { |
| "detail": "The agent capability surface exports traces, metrics, and logs and supports shared interception hooks for operational controls.", |
| "sourceIds": [ |
| "MAF-CAPABILITIES" |
| ], |
| "status": "native" |
| }, |
| "recovery": { |
| "detail": "Workflows resume or rehydrate from a checkpoint, including pending request state; durable storage must replace in-memory checkpoint storage.", |
| "sourceIds": [ |
| "MAF-CHECKPOINT", |
| "MAF-HITL" |
| ], |
| "status": "native" |
| }, |
| "state": { |
| "detail": "Checkpoint managers capture executor state, pending messages, requests, responses, and shared state at superstep boundaries.", |
| "sourceIds": [ |
| "MAF-CHECKPOINT" |
| ], |
| "status": "native" |
| } |
| }, |
| "constraints": "Feature parity varies by language and integration. Teams must verify the exact client surface because some UI workflow lifecycle events and resume controls are not mapped in the .NET AG-UI integration.", |
| "deploymentControl": [ |
| "self-host", |
| "managed" |
| ], |
| "disqualifiers": [ |
| "The team requires identical workflow lifecycle behavior across every language client", |
| "The deployment platform must avoid Microsoft-specific operational dependencies" |
| ], |
| "id": "microsoft-agent-framework", |
| "languages": [ |
| "python", |
| "dotnet", |
| "go" |
| ], |
| "license": "MIT", |
| "modelStrategy": [ |
| "agnostic", |
| "microsoft" |
| ], |
| "name": "Microsoft Agent Framework", |
| "orchestrationModel": "Agent abstractions plus graph workflows, orchestrations, request ports, middleware, and checkpoint managers.", |
| "priorities": [ |
| "durable-recovery", |
| "approval", |
| "observability", |
| "microsoft-stack", |
| "multi-language" |
| ], |
| "release": { |
| "publishedAt": "2026-08-28", |
| "sourceId": "MAF-REL", |
| "track": "Python", |
| "version": "1.16.0" |
| }, |
| "tradeoffs": [ |
| "Typed enterprise integration and multi-language workflow support", |
| "Fast-moving APIs require pinned versions and migration tests", |
| "Azure alignment is strongest, although the framework supports multiple providers" |
| ] |
| }, |
| { |
| "bestFor": "Python teams composing role-oriented multi-agent business processes that benefit from a concise crew abstraction and Flow-based approval routing.", |
| "capabilities": { |
| "approval": { |
| "detail": "The human_feedback decorator pauses a Flow for review and can route approved, rejected, or revision outcomes to distinct listeners.", |
| "sourceIds": [ |
| "CR-FLOWS" |
| ], |
| "status": "native" |
| }, |
| "deployment": { |
| "detail": "Python services can self-host crews and Flows; AMP offers GitHub-, CLI-, or Studio-driven managed deployments with generated API access.", |
| "sourceIds": [ |
| "CR-AMP", |
| "CR-PROD" |
| ], |
| "status": "self-host-or-managed" |
| }, |
| "observability": { |
| "detail": "CrewAI exposes execution tracing, while the managed AMP platform adds detailed traces, logs, team controls, and production monitoring.", |
| "sourceIds": [ |
| "CR-DOCS", |
| "CR-AMP" |
| ], |
| "status": "integrated" |
| }, |
| "recovery": { |
| "detail": "Persisted Flow state survives restarts and can resume by ID, but the deployment must supply durable storage and idempotent side-effect handling.", |
| "sourceIds": [ |
| "CR-FLOWS", |
| "CR-PROD" |
| ], |
| "status": "configured" |
| }, |
| "state": { |
| "detail": "The persist decorator stores Flow state, uses SQLite by default, and supports resuming or forking a persisted flow by state identifier.", |
| "sourceIds": [ |
| "CR-FLOWS" |
| ], |
| "status": "native" |
| } |
| }, |
| "constraints": "Crews and Flows are different orchestration layers. Durable production behavior depends on configuring persistence and the surrounding runtime rather than assuming a crew definition is automatically recoverable.", |
| "deploymentControl": [ |
| "self-host", |
| "managed" |
| ], |
| "disqualifiers": [ |
| "The core application stack is TypeScript or .NET with no Python service boundary", |
| "The workflow requires graph-level checkpoint semantics to be the primary programming model" |
| ], |
| "id": "crewai", |
| "languages": [ |
| "python" |
| ], |
| "license": "MIT", |
| "modelStrategy": [ |
| "agnostic" |
| ], |
| "name": "CrewAI", |
| "orchestrationModel": "Role-based crews for agent collaboration plus event-driven Flows for routing, persistence, and approval points.", |
| "priorities": [ |
| "multi-agent", |
| "approval", |
| "rapid-composition", |
| "managed-cloud" |
| ], |
| "release": { |
| "publishedAt": "2026-08-27", |
| "sourceId": "CR-REL", |
| "track": "Python", |
| "version": "1.15.18" |
| }, |
| "tradeoffs": [ |
| "Concise role and task composition", |
| "Flows add operational control but create a second mental model", |
| "Managed deployment and full observability are concentrated in CrewAI AMP" |
| ] |
| }, |
| { |
| "bestFor": "Python products centered on OpenAI models that need a small orchestration surface, built-in tracing, sessions, guardrails, and tool approval.", |
| "capabilities": { |
| "approval": { |
| "detail": "Tools can require approval, paused RunState can be serialized, and unresolved interruptions remain pending until approved or rejected.", |
| "sourceIds": [ |
| "OA-HITL" |
| ], |
| "status": "native" |
| }, |
| "deployment": { |
| "detail": "The SDK runs inside a Python application; teams choose their hosting and add a durable orchestrator when workflows span long waits or process restarts.", |
| "sourceIds": [ |
| "OA-RUN" |
| ], |
| "status": "application-owned" |
| }, |
| "observability": { |
| "detail": "Built-in tracing records model generations, tool calls, handoffs, guardrails, and custom events with controls for sensitive trace content.", |
| "sourceIds": [ |
| "OA-TRACE" |
| ], |
| "status": "native" |
| }, |
| "recovery": { |
| "detail": "Serialized run state supports approval pause/resume; process-restart durability and automatic recovery use documented Dapr, Temporal, Restate, or DBOS integrations.", |
| "sourceIds": [ |
| "OA-HITL", |
| "OA-RUN" |
| ], |
| "status": "external-runtime" |
| }, |
| "state": { |
| "detail": "Sessions store conversation history through SQLite, Redis, SQLAlchemy, Dapr, encrypted, file-backed, or OpenAI-managed implementations.", |
| "sourceIds": [ |
| "OA-RUN" |
| ], |
| "status": "native" |
| } |
| }, |
| "constraints": "The SDK manages the agent loop, but durable long-running recovery comes through Dapr, Temporal, Restate, DBOS, or application infrastructure rather than a built-in workflow server.", |
| "deploymentControl": [ |
| "self-host", |
| "managed" |
| ], |
| "disqualifiers": [ |
| "Provider neutrality is a hard procurement requirement", |
| "The system needs a built-in graph runtime and checkpoint manager without external workflow infrastructure" |
| ], |
| "id": "openai-agents-sdk", |
| "languages": [ |
| "python" |
| ], |
| "license": "MIT", |
| "modelStrategy": [ |
| "openai", |
| "agnostic-with-adapters" |
| ], |
| "name": "OpenAI Agents SDK", |
| "orchestrationModel": "A compact runner with agents, tools, handoffs, guardrails, sessions, interruptions, and tracing.", |
| "priorities": [ |
| "approval", |
| "observability", |
| "minimal-sdk", |
| "openai-stack" |
| ], |
| "release": { |
| "publishedAt": "2026-08-19", |
| "sourceId": "OA-REL", |
| "track": "Python", |
| "version": "0.22.0" |
| }, |
| "tradeoffs": [ |
| "Small API surface and first-party OpenAI integrations", |
| "Provider adapters exist but the default path is OpenAI-first", |
| "Durability adds an external orchestration dependency" |
| ] |
| }, |
| { |
| "bestFor": "Multi-language teams using Gemini or Google Cloud that want one agent toolkit across local development, Agent Runtime, Cloud Run, and GKE.", |
| "capabilities": { |
| "approval": { |
| "detail": "Graph workflows can request external human input and resume through the workflow runtime rather than hiding approval inside prompt text.", |
| "sourceIds": [ |
| "GA-HITL", |
| "GA-RESUME" |
| ], |
| "status": "native" |
| }, |
| "deployment": { |
| "detail": "Documented targets include Agent Runtime, Cloud Run, and GKE, with CLI support for CI/CD, service identity, secrets, scaling, and observability.", |
| "sourceIds": [ |
| "GA-DEPLOY" |
| ], |
| "status": "self-host-or-managed" |
| }, |
| "observability": { |
| "detail": "OpenTelemetry instrumentation exports agent and tool traces; Google tooling connects traces to Cloud Trace and optional BigQuery analytics.", |
| "sourceIds": [ |
| "GA-OBS" |
| ], |
| "status": "integrated" |
| }, |
| "recovery": { |
| "detail": "Resumable workflow sessions persist execution progress and continue graph work after interruption when backed by a persistent session service.", |
| "sourceIds": [ |
| "GA-RESUME", |
| "GA-STATE" |
| ], |
| "status": "native" |
| }, |
| "state": { |
| "detail": "Serializable session state is persisted by DatabaseSessionService or VertexAiSessionService; InMemorySessionService loses state on restart.", |
| "sourceIds": [ |
| "GA-STATE" |
| ], |
| "status": "native" |
| } |
| }, |
| "constraints": "Session persistence depends on the selected SessionService, and operational features differ across SDK languages and deployment targets. In-memory sessions do not survive a restart.", |
| "deploymentControl": [ |
| "self-host", |
| "managed" |
| ], |
| "disqualifiers": [ |
| "The organization requires a cloud-neutral operations plane", |
| "The chosen language needs a feature that exists only in another ADK SDK" |
| ], |
| "id": "google-adk", |
| "languages": [ |
| "python", |
| "typescript", |
| "go", |
| "java", |
| "kotlin" |
| ], |
| "license": "Apache-2.0", |
| "modelStrategy": [ |
| "google", |
| "agnostic" |
| ], |
| "name": "Google ADK", |
| "orchestrationModel": "LLM agents, deterministic sequential/parallel/loop agents, and graph workflows backed by sessions and events.", |
| "priorities": [ |
| "multi-language", |
| "managed-cloud", |
| "observability", |
| "google-stack", |
| "multi-agent" |
| ], |
| "release": { |
| "publishedAt": "2026-08-26", |
| "sourceId": "GA-REL", |
| "track": "Python", |
| "version": "2.8.0" |
| }, |
| "tradeoffs": [ |
| "Broad language coverage and Google Cloud deployment paths", |
| "Strongest operational path assumes Google Cloud", |
| "Teams must verify feature parity for their chosen language" |
| ] |
| } |
| ], |
| "methodVersion": "1.0.0", |
| "schemaVersion": "1.0.0" |
| }, |
| "sources": { |
| "evidenceCutoff": "2026-08-28", |
| "schemaVersion": "1.0.0", |
| "sources": [ |
| { |
| "framework": "langgraph", |
| "id": "LG-REL", |
| "limitations": "The repository publishes several packages; this record identifies the Python langgraph core release rather than the separately versioned SDK or checkpoint packages.", |
| "publisher": "LangChain", |
| "sourceType": "official-release", |
| "supports": [ |
| "release", |
| "license" |
| ], |
| "title": "LangGraph 1.2.11 release", |
| "url": "https://github.com/langchain-ai/langgraph/releases/tag/1.2.11", |
| "verifiedOn": "2026-08-28" |
| }, |
| { |
| "framework": "langgraph", |
| "id": "LG-OVERVIEW", |
| "limitations": "Product overview language is used only for documented mechanisms; it is not evidence of comparative superiority, adoption, or outcome quality.", |
| "publisher": "LangChain", |
| "sourceType": "official-docs", |
| "supports": [ |
| "orchestration", |
| "observability", |
| "deployment" |
| ], |
| "title": "LangGraph overview", |
| "url": "https://docs.langchain.com/oss/python/langgraph/overview", |
| "verifiedOn": "2026-08-28" |
| }, |
| { |
| "framework": "langgraph", |
| "id": "LG-PERSIST", |
| "limitations": "InMemorySaver examples are development fixtures. Production durability depends on selecting and operating a persistent checkpointer backend.", |
| "publisher": "LangChain", |
| "sourceType": "official-docs", |
| "supports": [ |
| "state", |
| "recovery" |
| ], |
| "title": "LangGraph persistence", |
| "url": "https://docs.langchain.com/oss/python/langgraph/persistence", |
| "verifiedOn": "2026-08-28" |
| }, |
| { |
| "framework": "langgraph", |
| "id": "LG-HITL", |
| "limitations": "An interrupt supplies a pause/resume primitive. Approval policy, identity, authorization, timeout, and escalation remain application responsibilities.", |
| "publisher": "LangChain", |
| "sourceType": "official-docs", |
| "supports": [ |
| "approval", |
| "recovery" |
| ], |
| "title": "LangGraph interrupts", |
| "url": "https://docs.langchain.com/oss/python/langgraph/interrupts", |
| "verifiedOn": "2026-08-28" |
| }, |
| { |
| "framework": "langgraph", |
| "id": "LG-SERVER", |
| "limitations": "Agent Server is a deployment runtime with infrastructure and licensing considerations separate from the open-source graph library.", |
| "publisher": "LangChain", |
| "sourceType": "official-docs", |
| "supports": [ |
| "recovery", |
| "deployment" |
| ], |
| "title": "LangGraph Agent Server", |
| "url": "https://docs.langchain.com/langsmith/agent-server", |
| "verifiedOn": "2026-08-28" |
| }, |
| { |
| "framework": "langgraph", |
| "id": "LG-PRIVACY", |
| "limitations": "Storage and trace behavior depends on runtime configuration. Teams must verify telemetry, retention, encryption, and regional requirements for their deployment.", |
| "publisher": "LangChain", |
| "sourceType": "official-docs", |
| "supports": [ |
| "observability", |
| "state" |
| ], |
| "title": "LangSmith data storage and privacy", |
| "url": "https://docs.langchain.com/langsmith/data-storage-and-privacy", |
| "verifiedOn": "2026-08-28" |
| }, |
| { |
| "framework": "microsoft-agent-framework", |
| "id": "MAF-REL", |
| "limitations": "The repository versions language packages independently. This comparison stamps the Python track and does not imply identical version numbers across .NET and Go.", |
| "publisher": "Microsoft", |
| "sourceType": "official-release", |
| "supports": [ |
| "release", |
| "license" |
| ], |
| "title": "Microsoft Agent Framework Python 1.16.0 release", |
| "url": "https://github.com/microsoft/agent-framework/releases/tag/python-1.16.0", |
| "verifiedOn": "2026-08-28" |
| }, |
| { |
| "framework": "microsoft-agent-framework", |
| "id": "MAF-OVERVIEW", |
| "limitations": "The overview identifies product scope and succession from AutoGen and Semantic Kernel; it does not prove compatibility with every predecessor application.", |
| "publisher": "Microsoft Learn", |
| "sourceType": "official-docs", |
| "supports": [ |
| "orchestration", |
| "deployment", |
| "positioning" |
| ], |
| "title": "Microsoft Agent Framework overview", |
| "url": "https://learn.microsoft.com/en-us/agent-framework/overview/", |
| "verifiedOn": "2026-08-28" |
| }, |
| { |
| "framework": "microsoft-agent-framework", |
| "id": "MAF-CHECKPOINT", |
| "limitations": "Checkpoint availability does not make external side effects idempotent. Durable storage, schema migration, and replay-safe tool design remain deployment work.", |
| "publisher": "Microsoft Learn", |
| "sourceType": "official-docs", |
| "supports": [ |
| "state", |
| "recovery" |
| ], |
| "title": "Agent Framework workflow checkpoints", |
| "url": "https://learn.microsoft.com/en-us/agent-framework/workflows/checkpoints", |
| "verifiedOn": "2026-08-28" |
| }, |
| { |
| "framework": "microsoft-agent-framework", |
| "id": "MAF-HITL", |
| "limitations": "Request and approval primitives do not define the enterprise approver identity, authorization policy, response deadline, or separation-of-duties rules.", |
| "publisher": "Microsoft Learn", |
| "sourceType": "official-docs", |
| "supports": [ |
| "approval", |
| "recovery" |
| ], |
| "title": "Agent Framework workflow human-in-the-loop", |
| "url": "https://learn.microsoft.com/en-us/agent-framework/workflows/human-in-the-loop", |
| "verifiedOn": "2026-08-28" |
| }, |
| { |
| "framework": "microsoft-agent-framework", |
| "id": "MAF-CAPABILITIES", |
| "limitations": "A capability catalog confirms supported surfaces, not their readiness for a particular workload, language binding, compliance regime, or operations team.", |
| "publisher": "Microsoft Learn", |
| "sourceType": "official-docs", |
| "supports": [ |
| "observability", |
| "governance" |
| ], |
| "title": "Agent capabilities", |
| "url": "https://learn.microsoft.com/en-us/agent-framework/agents/", |
| "verifiedOn": "2026-08-28" |
| }, |
| { |
| "framework": "crewai", |
| "id": "CR-REL", |
| "limitations": "The release tag stamps the Python package. Managed AMP capabilities have a separate service lifecycle and are not versioned by this tag.", |
| "publisher": "CrewAI", |
| "sourceType": "official-release", |
| "supports": [ |
| "release", |
| "license" |
| ], |
| "title": "CrewAI 1.15.18 release", |
| "url": "https://github.com/crewAIInc/crewAI/releases/tag/1.15.18", |
| "verifiedOn": "2026-08-28" |
| }, |
| { |
| "framework": "crewai", |
| "id": "CR-DOCS", |
| "limitations": "The documentation landing page describes the combined open-source and enterprise journey; individual deployment entitlements require separate verification.", |
| "publisher": "CrewAI", |
| "sourceType": "official-docs", |
| "supports": [ |
| "orchestration", |
| "observability" |
| ], |
| "title": "CrewAI documentation", |
| "url": "https://docs.crewai.com/", |
| "verifiedOn": "2026-08-28" |
| }, |
| { |
| "framework": "crewai", |
| "id": "CR-FLOWS", |
| "limitations": "SQLite persistence is a default mechanism, not a production architecture. The application must select durable storage and define idempotent effects and approval delivery.", |
| "publisher": "CrewAI", |
| "sourceType": "official-docs", |
| "supports": [ |
| "state", |
| "approval", |
| "recovery" |
| ], |
| "title": "CrewAI Flows", |
| "url": "https://docs.crewai.com/en/concepts/flows", |
| "verifiedOn": "2026-08-28" |
| }, |
| { |
| "framework": "crewai", |
| "id": "CR-AMP", |
| "limitations": "AMP is a managed commercial platform. Its presence does not mean the open-source package includes every managed monitoring, access, or scaling function.", |
| "publisher": "CrewAI", |
| "sourceType": "official-docs", |
| "supports": [ |
| "observability", |
| "deployment" |
| ], |
| "title": "CrewAI AMP introduction", |
| "url": "https://docs.crewai.com/enterprise/introduction", |
| "verifiedOn": "2026-08-28" |
| }, |
| { |
| "framework": "crewai", |
| "id": "CR-PROD", |
| "limitations": "This versioned repository document predates the comparison cutoff release. It supports the architectural boundary only; current commands must be checked against live docs.", |
| "publisher": "CrewAI", |
| "sourceType": "official-repository-docs", |
| "supports": [ |
| "recovery", |
| "deployment" |
| ], |
| "title": "CrewAI production architecture", |
| "url": "https://github.com/crewAIInc/crewAI/blob/main/docs/v1.12.2/en/concepts/production-architecture.mdx", |
| "verifiedOn": "2026-08-28" |
| }, |
| { |
| "framework": "openai-agents-sdk", |
| "id": "OA-REL", |
| "limitations": "This tag covers the Python SDK. Other OpenAI agent products and language SDKs have distinct release surfaces and are outside this version stamp.", |
| "publisher": "OpenAI", |
| "sourceType": "official-release", |
| "supports": [ |
| "release", |
| "license" |
| ], |
| "title": "OpenAI Agents SDK Python 0.22.0 release", |
| "url": "https://github.com/openai/openai-agents-python/releases/tag/v0.22.0", |
| "verifiedOn": "2026-08-28" |
| }, |
| { |
| "framework": "openai-agents-sdk", |
| "id": "OA-RUN", |
| "limitations": "Durable execution integrations add separate runtimes and operating models. The SDK alone does not operate their queues, storage, retries, or worker lifecycle.", |
| "publisher": "OpenAI", |
| "sourceType": "official-docs", |
| "supports": [ |
| "state", |
| "recovery", |
| "deployment" |
| ], |
| "title": "Running agents", |
| "url": "https://openai.github.io/openai-agents-python/running_agents/", |
| "verifiedOn": "2026-08-28" |
| }, |
| { |
| "framework": "openai-agents-sdk", |
| "id": "OA-HITL", |
| "limitations": "Tool approval is a mechanism, not a complete governance policy. Identity, authorization, separation of duties, escalation, and audit retention remain application concerns.", |
| "publisher": "OpenAI", |
| "sourceType": "official-docs", |
| "supports": [ |
| "approval", |
| "state" |
| ], |
| "title": "Human-in-the-loop", |
| "url": "https://openai.github.io/openai-agents-python/human_in_the_loop/", |
| "verifiedOn": "2026-08-28" |
| }, |
| { |
| "framework": "openai-agents-sdk", |
| "id": "OA-TRACE", |
| "limitations": "Trace content can include sensitive data depending on configuration. Enterprises must set capture, export, retention, and access policies before production use.", |
| "publisher": "OpenAI", |
| "sourceType": "official-docs", |
| "supports": [ |
| "observability" |
| ], |
| "title": "Tracing", |
| "url": "https://openai.github.io/openai-agents-python/tracing/", |
| "verifiedOn": "2026-08-28" |
| }, |
| { |
| "framework": "google-adk", |
| "id": "GA-REL", |
| "limitations": "This tag stamps the Python SDK. TypeScript, Go, Java, and Kotlin bindings are separate packages and may expose different features or release numbers.", |
| "publisher": "Google", |
| "sourceType": "official-release", |
| "supports": [ |
| "release", |
| "license" |
| ], |
| "title": "Google ADK Python 2.8.0 release", |
| "url": "https://github.com/google/adk-python/releases/tag/v2.8.0", |
| "verifiedOn": "2026-08-28" |
| }, |
| { |
| "framework": "google-adk", |
| "id": "GA-STATE", |
| "limitations": "Persistence depends on SessionService. In-memory state is explicitly non-durable and cannot support process-restart recovery.", |
| "publisher": "Google", |
| "sourceType": "official-docs", |
| "supports": [ |
| "state", |
| "recovery" |
| ], |
| "title": "ADK session state", |
| "url": "https://google.github.io/adk-docs/sessions/state/", |
| "verifiedOn": "2026-08-28" |
| }, |
| { |
| "framework": "google-adk", |
| "id": "GA-HITL", |
| "limitations": "Human input primitives need application-specific identity, authorization, UI, timeout, and escalation handling to become an enterprise approval control.", |
| "publisher": "Google", |
| "sourceType": "official-docs", |
| "supports": [ |
| "approval" |
| ], |
| "title": "ADK graph human input", |
| "url": "https://google.github.io/adk-docs/graphs/human-input/", |
| "verifiedOn": "2026-08-28" |
| }, |
| { |
| "framework": "google-adk", |
| "id": "GA-RESUME", |
| "limitations": "Resume behavior depends on a resumable workflow and persistent session implementation. Teams must test replay behavior around external side effects.", |
| "publisher": "Google", |
| "sourceType": "official-docs", |
| "supports": [ |
| "approval", |
| "recovery" |
| ], |
| "title": "ADK runtime resume", |
| "url": "https://google.github.io/adk-docs/runtime/resume/", |
| "verifiedOn": "2026-08-28" |
| }, |
| { |
| "framework": "google-adk", |
| "id": "GA-OBS", |
| "limitations": "Cloud Trace and analytics behavior depends on deployment and capture configuration. Full prompt-response logging has additional privacy and storage implications.", |
| "publisher": "Google", |
| "sourceType": "official-docs", |
| "supports": [ |
| "observability" |
| ], |
| "title": "ADK observability", |
| "url": "https://google.github.io/adk-docs/observability/", |
| "verifiedOn": "2026-08-28" |
| }, |
| { |
| "framework": "google-adk", |
| "id": "GA-DEPLOY", |
| "limitations": "Agent Runtime, Cloud Run, and GKE have different cost, identity, networking, scaling, and operational boundaries; listing them is not a recommendation.", |
| "publisher": "Google", |
| "sourceType": "official-docs", |
| "supports": [ |
| "deployment" |
| ], |
| "title": "ADK deployment options", |
| "url": "https://google.github.io/adk-docs/deploy/", |
| "verifiedOn": "2026-08-28" |
| } |
| ] |
| } |
| }, |
| "rag": { |
| "claims": { |
| "claims": [ |
| { |
| "id": "CLAIM-RAG-RUNTIME-RETRIEVAL", |
| "sourceIds": [ |
| "SOURCE-RAG-PAPER", |
| "SOURCE-MICROSOFT-RAG-VS-FT" |
| ], |
| "statement": "RAG supplies retrieved external information to a generator at inference time instead of encoding every changing fact in model parameters.", |
| "status": "VERIFIED" |
| }, |
| { |
| "id": "CLAIM-RAG-FRESHNESS-FIT", |
| "sourceIds": [ |
| "SOURCE-MICROSOFT-RAG-VS-FT", |
| "SOURCE-PHAROS-GUIDE" |
| ], |
| "statement": "A retrieval path is a suitable candidate when the required knowledge changes independently of the model and the corpus can be maintained.", |
| "status": "BOUNDED" |
| }, |
| { |
| "id": "CLAIM-RAG-PROVENANCE", |
| "sourceIds": [ |
| "SOURCE-RAG-PAPER", |
| "SOURCE-ANTHROPIC-CONTEXTUAL-RETRIEVAL" |
| ], |
| "statement": "Retrieved passages can provide provenance for an answer, but the application must still test whether each citation supports the generated claim.", |
| "status": "BOUNDED" |
| }, |
| { |
| "id": "CLAIM-FINE-TUNING-BEHAVIOR", |
| "sourceIds": [ |
| "SOURCE-MICROSOFT-RAG-VS-FT", |
| "SOURCE-OPENAI-MODEL-OPTIMIZATION" |
| ], |
| "statement": "Fine-tuning is an optimization path for task behavior, format, or specialization; it is not a dependable mechanism for continuously changing facts.", |
| "status": "VERIFIED" |
| }, |
| { |
| "id": "CLAIM-EXAMPLES-REQUIRED", |
| "sourceIds": [ |
| "SOURCE-OPENAI-MODEL-OPTIMIZATION", |
| "SOURCE-MICROSOFT-RAG-VS-FT" |
| ], |
| "statement": "A fine-tuning decision needs representative examples and an evaluation set that reflects the intended task.", |
| "status": "VERIFIED" |
| }, |
| { |
| "id": "CLAIM-EVAL-FIRST", |
| "sourceIds": [ |
| "SOURCE-OPENAI-MODEL-OPTIMIZATION", |
| "SOURCE-PHAROS-GUIDE" |
| ], |
| "statement": "Prompting, retrieval, and training changes should be compared against a defined evaluation baseline rather than selected from an architecture label alone.", |
| "status": "BOUNDED" |
| }, |
| { |
| "id": "CLAIM-LEXICAL-SEMANTIC", |
| "sourceIds": [ |
| "SOURCE-ANTHROPIC-CONTEXTUAL-RETRIEVAL" |
| ], |
| "statement": "Lexical retrieval such as BM25 and embedding retrieval recover different signals and can be combined when exact terms and semantic similarity both matter.", |
| "status": "VERIFIED" |
| }, |
| { |
| "id": "CLAIM-RERANKING", |
| "sourceIds": [ |
| "SOURCE-ANTHROPIC-CONTEXTUAL-RETRIEVAL" |
| ], |
| "statement": "A reranker can reorder an initial retrieval candidate set before context is passed to the model.", |
| "status": "VERIFIED" |
| }, |
| { |
| "id": "CLAIM-CORPUS-QUALITY", |
| "sourceIds": [ |
| "SOURCE-MICROSOFT-RAG-VS-FT", |
| "SOURCE-PHAROS-GUIDE" |
| ], |
| "statement": "A RAG recommendation presumes an authoritative, permissioned corpus and an operating path for indexing and updates.", |
| "status": "BOUNDED" |
| }, |
| { |
| "id": "CLAIM-LORA-METHOD", |
| "sourceIds": [ |
| "SOURCE-LORA-PAPER" |
| ], |
| "statement": "LoRA is a parameter-efficient adaptation method that freezes pretrained weights and learns low-rank update matrices.", |
| "status": "VERIFIED" |
| }, |
| { |
| "id": "CLAIM-HYBRID-PATTERN", |
| "sourceIds": [ |
| "SOURCE-RAFT-PAPER", |
| "SOURCE-MICROSOFT-RAG-VS-FT" |
| ], |
| "statement": "Retrieval and parameter adaptation can be combined when a workload separately requires external domain evidence and specialized model behavior.", |
| "status": "BOUNDED" |
| }, |
| { |
| "id": "CLAIM-RAFT-SCOPE", |
| "sourceIds": [ |
| "SOURCE-RAFT-PAPER" |
| ], |
| "statement": "RAFT is one retrieval-aware fine-tuning method that trains with relevant documents and distractor documents for domain-specific RAG settings.", |
| "status": "VERIFIED" |
| }, |
| { |
| "id": "CLAIM-DUAL-OPERATIONS", |
| "sourceIds": [ |
| "SOURCE-RAFT-PAPER", |
| "SOURCE-PHAROS-GUIDE" |
| ], |
| "statement": "A hybrid design creates both retrieval-index and model-version lifecycle work, so both operating capabilities must be assessed.", |
| "status": "BOUNDED" |
| }, |
| { |
| "id": "CLAIM-PRIVACY-TOPOLOGY", |
| "sourceIds": [ |
| "SOURCE-OPENAI-DATA-CONTROLS", |
| "SOURCE-MICROSOFT-RAG-VS-FT" |
| ], |
| "statement": "Privacy and retention properties depend on the selected provider, endpoint, storage path, deployment topology, and configuration; neither architecture has one universal data boundary.", |
| "status": "BOUNDED" |
| }, |
| { |
| "id": "CLAIM-RUNTIME-MEASUREMENT", |
| "sourceIds": [ |
| "SOURCE-MICROSOFT-RAG-VS-FT", |
| "SOURCE-OPENAI-MODEL-OPTIMIZATION" |
| ], |
| "statement": "Latency, quality, and cost tradeoffs must be measured on the target workload and deployment rather than inferred from RAG or fine-tuning labels.", |
| "status": "BOUNDED" |
| } |
| ], |
| "version": "2026-08-18.1" |
| }, |
| "criteria": { |
| "answerSemantics": { |
| "no": "Evidence contradicts the condition for the target workload.", |
| "unknown": "The condition has not been verified; the engine will not infer an answer.", |
| "yes": "Evidence supports the condition for the target workload." |
| }, |
| "criteria": [ |
| { |
| "allowedAnswers": [ |
| "yes", |
| "no", |
| "unknown" |
| ], |
| "claimIds": [ |
| "CLAIM-RAG-FRESHNESS-FIT" |
| ], |
| "core": true, |
| "dimension": "knowledge", |
| "id": "fresh_facts", |
| "label": "Changing knowledge", |
| "question": "Must answers use facts that change independently of the model release?", |
| "sourceIds": [ |
| "SOURCE-MICROSOFT-RAG-VS-FT" |
| ] |
| }, |
| { |
| "allowedAnswers": [ |
| "yes", |
| "no", |
| "unknown" |
| ], |
| "claimIds": [ |
| "CLAIM-RAG-PROVENANCE" |
| ], |
| "core": true, |
| "dimension": "knowledge", |
| "id": "source_citations", |
| "label": "Passage-level evidence", |
| "question": "Must users inspect source passages behind material answer claims?", |
| "sourceIds": [ |
| "SOURCE-RAG-PAPER" |
| ] |
| }, |
| { |
| "allowedAnswers": [ |
| "yes", |
| "no", |
| "unknown" |
| ], |
| "claimIds": [ |
| "CLAIM-FINE-TUNING-BEHAVIOR" |
| ], |
| "core": true, |
| "dimension": "behavior", |
| "id": "behavior_change", |
| "label": "Behavior specialization", |
| "question": "Is the primary gap consistent task behavior, style, format, classification, or extraction?", |
| "sourceIds": [ |
| "SOURCE-OPENAI-MODEL-OPTIMIZATION" |
| ] |
| }, |
| { |
| "allowedAnswers": [ |
| "yes", |
| "no", |
| "unknown" |
| ], |
| "claimIds": [ |
| "CLAIM-EXAMPLES-REQUIRED" |
| ], |
| "core": true, |
| "dimension": "data", |
| "id": "representative_examples", |
| "label": "Representative examples", |
| "question": "Are representative, permitted examples available for training and held-out evaluation?", |
| "sourceIds": [ |
| "SOURCE-OPENAI-MODEL-OPTIMIZATION" |
| ] |
| }, |
| { |
| "allowedAnswers": [ |
| "yes", |
| "no", |
| "unknown" |
| ], |
| "claimIds": [ |
| "CLAIM-EVAL-FIRST" |
| ], |
| "core": true, |
| "dimension": "evaluation", |
| "id": "evaluation_set", |
| "label": "Evaluation set", |
| "question": "Is there a versioned evaluation set with acceptance criteria for this workload?", |
| "sourceIds": [ |
| "SOURCE-OPENAI-MODEL-OPTIMIZATION" |
| ] |
| }, |
| { |
| "allowedAnswers": [ |
| "yes", |
| "no", |
| "unknown" |
| ], |
| "claimIds": [ |
| "CLAIM-CORPUS-QUALITY" |
| ], |
| "core": true, |
| "dimension": "data", |
| "id": "authoritative_corpus", |
| "label": "Authoritative corpus", |
| "question": "Is an authoritative, permissioned, maintainable corpus available for retrieval?", |
| "sourceIds": [ |
| "SOURCE-MICROSOFT-RAG-VS-FT" |
| ] |
| }, |
| { |
| "allowedAnswers": [ |
| "yes", |
| "no", |
| "unknown" |
| ], |
| "claimIds": [ |
| "CLAIM-LEXICAL-SEMANTIC" |
| ], |
| "core": false, |
| "dimension": "retrieval", |
| "id": "exact_term_retrieval", |
| "label": "Exact-term retrieval", |
| "question": "Do identifiers, codes, names, or exact phrases materially affect retrieval quality?", |
| "sourceIds": [ |
| "SOURCE-ANTHROPIC-CONTEXTUAL-RETRIEVAL" |
| ] |
| }, |
| { |
| "allowedAnswers": [ |
| "yes", |
| "no", |
| "unknown" |
| ], |
| "claimIds": [ |
| "CLAIM-RUNTIME-MEASUREMENT" |
| ], |
| "core": false, |
| "dimension": "operations", |
| "id": "strict_runtime_budget", |
| "label": "Strict runtime budget", |
| "question": "Does the workload have a strict measured latency or per-request cost budget?", |
| "sourceIds": [ |
| "SOURCE-MICROSOFT-RAG-VS-FT" |
| ] |
| }, |
| { |
| "allowedAnswers": [ |
| "yes", |
| "no", |
| "unknown" |
| ], |
| "claimIds": [ |
| "CLAIM-PRIVACY-TOPOLOGY" |
| ], |
| "core": false, |
| "dimension": "governance", |
| "id": "privacy_topology", |
| "label": "Verified privacy topology", |
| "question": "Have storage, retrieval, training, retention, and provider data paths been mapped?", |
| "sourceIds": [ |
| "SOURCE-OPENAI-DATA-CONTROLS" |
| ] |
| }, |
| { |
| "allowedAnswers": [ |
| "yes", |
| "no", |
| "unknown" |
| ], |
| "claimIds": [ |
| "CLAIM-CORPUS-QUALITY" |
| ], |
| "core": true, |
| "dimension": "operations", |
| "id": "retrieval_operations", |
| "label": "Retrieval operations", |
| "question": "Can the team own corpus ingestion, index updates, retrieval evaluation, and rollback?", |
| "sourceIds": [ |
| "SOURCE-MICROSOFT-RAG-VS-FT" |
| ] |
| }, |
| { |
| "allowedAnswers": [ |
| "yes", |
| "no", |
| "unknown" |
| ], |
| "claimIds": [ |
| "CLAIM-EXAMPLES-REQUIRED", |
| "CLAIM-EVAL-FIRST" |
| ], |
| "core": true, |
| "dimension": "operations", |
| "id": "training_operations", |
| "label": "Training operations", |
| "question": "Can the team own training data, model versions, regression evaluation, and rollback?", |
| "sourceIds": [ |
| "SOURCE-OPENAI-MODEL-OPTIMIZATION" |
| ] |
| }, |
| { |
| "allowedAnswers": [ |
| "yes", |
| "no", |
| "unknown" |
| ], |
| "claimIds": [ |
| "CLAIM-FINE-TUNING-BEHAVIOR" |
| ], |
| "core": true, |
| "dimension": "behavior", |
| "id": "stable_narrow_task", |
| "label": "Stable narrow task", |
| "question": "Is the target behavior narrow enough and stable enough to maintain as a trained model contract?", |
| "sourceIds": [ |
| "SOURCE-MICROSOFT-RAG-VS-FT" |
| ] |
| } |
| ], |
| "version": "2026-08-18.1" |
| }, |
| "methodology": { |
| "allowedAnswers": [ |
| "yes", |
| "no", |
| "unknown" |
| ], |
| "decisionProcedure": [ |
| "Validate that every registered criterion has exactly one allowed answer and reject unknown criterion IDs.", |
| "Evaluate decision rules in ascending unique priority and select the first matching outcome.", |
| "Evaluate advisory rules independently and attach their tests, risks, and provenance without changing the outcome.", |
| "Export the answers, winning rule, triggered advisories, blockers, unresolved inputs, source IDs, claim IDs, and registry versions." |
| ], |
| "disclosure": "Research synthesis and implementation were AI-assisted; claims are bounded to the listed sources and verification date.", |
| "evaluationContract": { |
| "minimumJobs": [ |
| "Define representative tasks and failure classes.", |
| "Record a prompt-only or current-system baseline.", |
| "For retrieval, test retrieval relevance and claim-to-passage support separately from final-answer quality.", |
| "For tuning, test target behavior and regression on capabilities that must be preserved.", |
| "Measure latency, cost, privacy paths, and operational recovery in the intended topology." |
| ], |
| "requiredBeforeWinner": true |
| }, |
| "limitations": [ |
| "The outcome is a candidate for a bounded experiment, not an implementation guarantee.", |
| "The method contains no universal dataset-size, accuracy, latency, document-count, or cost threshold.", |
| "Provider features and data controls can change after the dated source verification.", |
| "Independent human technical review of this microsite is not claimed." |
| ], |
| "name": "Evidence-bound RAG versus fine-tuning decision method", |
| "outcomes": [ |
| { |
| "id": "RAG", |
| "label": "RAG candidate", |
| "meaning": "Test a retrieval-grounded baseline because changing or traceable knowledge is required and the corpus and retrieval operating path exist." |
| }, |
| { |
| "id": "FINE_TUNING", |
| "label": "Fine-tuning candidate", |
| "meaning": "Test parameter adaptation because the primary need is stable task behavior and representative examples and training operations exist." |
| }, |
| { |
| "id": "HYBRID", |
| "label": "Hybrid candidate", |
| "meaning": "Test retrieval plus behavior adaptation because both needs and both operational paths are independently evidenced." |
| }, |
| { |
| "id": "BASELINE_FIRST", |
| "label": "Baseline first", |
| "meaning": "Establish or improve a prompting/evaluation baseline before adding retrieval or training complexity." |
| }, |
| { |
| "id": "BLOCKED", |
| "label": "Blocked", |
| "meaning": "A required data or operating dependency is absent; a plausible architecture label cannot remove that blocker." |
| }, |
| { |
| "id": "INSUFFICIENT_EVIDENCE", |
| "label": "Insufficient evidence", |
| "meaning": "A core input or evaluation contract is unresolved, so the method declines to select a winner." |
| } |
| ], |
| "priorityDirection": "ascending", |
| "publishedOn": "2026-08-18", |
| "publisher": "Pharos Production", |
| "purpose": "Classify the next architecture experiment without converting qualitative evidence into a probability or universal score.", |
| "version": "2026-08-18.1" |
| }, |
| "rules": { |
| "rules": [ |
| { |
| "claimIds": [ |
| "CLAIM-RAG-FRESHNESS-FIT", |
| "CLAIM-CORPUS-QUALITY" |
| ], |
| "disqualifiers": [ |
| "NO_AUTHORITATIVE_CORPUS" |
| ], |
| "id": "RULE-BLOCKED-NO-CORPUS", |
| "kind": "decision", |
| "nextSteps": [ |
| "Identify an authoritative, permissioned corpus and an owner for updates before selecting a knowledge architecture." |
| ], |
| "outcomeId": "BLOCKED", |
| "priority": 10, |
| "rejectedOutcomes": [ |
| "RAG", |
| "HYBRID", |
| "FINE_TUNING" |
| ], |
| "sourceIds": [ |
| "SOURCE-MICROSOFT-RAG-VS-FT", |
| "SOURCE-RAG-PAPER" |
| ], |
| "when": { |
| "all": [ |
| { |
| "criterionId": "authoritative_corpus", |
| "operator": "equals", |
| "value": "no" |
| } |
| ], |
| "any": [ |
| { |
| "criterionId": "fresh_facts", |
| "operator": "equals", |
| "value": "yes" |
| }, |
| { |
| "criterionId": "source_citations", |
| "operator": "equals", |
| "value": "yes" |
| } |
| ] |
| } |
| }, |
| { |
| "claimIds": [ |
| "CLAIM-EVAL-FIRST" |
| ], |
| "id": "RULE-INSUFFICIENT-UNKNOWN-CORE", |
| "kind": "decision", |
| "nextSteps": [ |
| "Resolve every unknown core input with a named evidence owner before forcing an architecture choice." |
| ], |
| "outcomeId": "INSUFFICIENT_EVIDENCE", |
| "priority": 20, |
| "sourceIds": [ |
| "SOURCE-OPENAI-MODEL-OPTIMIZATION" |
| ], |
| "when": { |
| "any": [ |
| { |
| "criterionId": "fresh_facts", |
| "operator": "equals", |
| "value": "unknown" |
| }, |
| { |
| "criterionId": "source_citations", |
| "operator": "equals", |
| "value": "unknown" |
| }, |
| { |
| "criterionId": "behavior_change", |
| "operator": "equals", |
| "value": "unknown" |
| }, |
| { |
| "criterionId": "representative_examples", |
| "operator": "equals", |
| "value": "unknown" |
| }, |
| { |
| "criterionId": "evaluation_set", |
| "operator": "equals", |
| "value": "unknown" |
| }, |
| { |
| "criterionId": "authoritative_corpus", |
| "operator": "equals", |
| "value": "unknown" |
| }, |
| { |
| "criterionId": "retrieval_operations", |
| "operator": "equals", |
| "value": "unknown" |
| }, |
| { |
| "criterionId": "training_operations", |
| "operator": "equals", |
| "value": "unknown" |
| }, |
| { |
| "criterionId": "stable_narrow_task", |
| "operator": "equals", |
| "value": "unknown" |
| } |
| ] |
| } |
| }, |
| { |
| "claimIds": [ |
| "CLAIM-EVAL-FIRST", |
| "CLAIM-EXAMPLES-REQUIRED" |
| ], |
| "disqualifiers": [ |
| "NO_EVALUATION_SET" |
| ], |
| "id": "RULE-INSUFFICIENT-NO-EVAL", |
| "kind": "decision", |
| "nextSteps": [ |
| "Create a versioned task evaluation set and acceptance criteria, then compare candidates against the same baseline." |
| ], |
| "outcomeId": "INSUFFICIENT_EVIDENCE", |
| "priority": 30, |
| "sourceIds": [ |
| "SOURCE-OPENAI-MODEL-OPTIMIZATION" |
| ], |
| "when": { |
| "all": [ |
| { |
| "criterionId": "evaluation_set", |
| "operator": "equals", |
| "value": "no" |
| } |
| ] |
| } |
| }, |
| { |
| "claimIds": [ |
| "CLAIM-EXAMPLES-REQUIRED", |
| "CLAIM-EVAL-FIRST" |
| ], |
| "id": "RULE-BASELINE-NO-EXAMPLES", |
| "kind": "decision", |
| "nextSteps": [ |
| "Establish a prompt baseline and collect permitted, representative failure and success examples before training." |
| ], |
| "outcomeId": "BASELINE_FIRST", |
| "priority": 40, |
| "rejectedOutcomes": [ |
| "FINE_TUNING", |
| "HYBRID" |
| ], |
| "sourceIds": [ |
| "SOURCE-OPENAI-MODEL-OPTIMIZATION" |
| ], |
| "when": { |
| "all": [ |
| { |
| "criterionId": "behavior_change", |
| "operator": "equals", |
| "value": "yes" |
| }, |
| { |
| "criterionId": "representative_examples", |
| "operator": "equals", |
| "value": "no" |
| } |
| ] |
| } |
| }, |
| { |
| "claimIds": [ |
| "CLAIM-CORPUS-QUALITY" |
| ], |
| "disqualifiers": [ |
| "NO_RETRIEVAL_OPERATING_PATH" |
| ], |
| "id": "RULE-BLOCKED-NO-RETRIEVAL-OPS", |
| "kind": "decision", |
| "nextSteps": [ |
| "Assign ownership for ingestion, index freshness, retrieval evaluation, rollback, and access control." |
| ], |
| "outcomeId": "BLOCKED", |
| "priority": 50, |
| "rejectedOutcomes": [ |
| "RAG", |
| "HYBRID" |
| ], |
| "sourceIds": [ |
| "SOURCE-MICROSOFT-RAG-VS-FT" |
| ], |
| "when": { |
| "all": [ |
| { |
| "criterionId": "authoritative_corpus", |
| "operator": "equals", |
| "value": "yes" |
| }, |
| { |
| "criterionId": "retrieval_operations", |
| "operator": "equals", |
| "value": "no" |
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| ], |
| "any": [ |
| { |
| "criterionId": "fresh_facts", |
| "operator": "equals", |
| "value": "yes" |
| }, |
| { |
| "criterionId": "source_citations", |
| "operator": "equals", |
| "value": "yes" |
| } |
| ] |
| } |
| }, |
| { |
| "claimIds": [ |
| "CLAIM-EXAMPLES-REQUIRED", |
| "CLAIM-EVAL-FIRST" |
| ], |
| "disqualifiers": [ |
| "NO_TRAINING_OPERATING_PATH" |
| ], |
| "id": "RULE-BLOCKED-NO-TRAINING-OPS", |
| "kind": "decision", |
| "nextSteps": [ |
| "Assign ownership for datasets, model versions, regression evaluation, rollback, and provider lifecycle." |
| ], |
| "outcomeId": "BLOCKED", |
| "priority": 60, |
| "rejectedOutcomes": [ |
| "FINE_TUNING", |
| "HYBRID" |
| ], |
| "sourceIds": [ |
| "SOURCE-OPENAI-MODEL-OPTIMIZATION" |
| ], |
| "when": { |
| "all": [ |
| { |
| "criterionId": "behavior_change", |
| "operator": "equals", |
| "value": "yes" |
| }, |
| { |
| "criterionId": "representative_examples", |
| "operator": "equals", |
| "value": "yes" |
| }, |
| { |
| "criterionId": "training_operations", |
| "operator": "equals", |
| "value": "no" |
| } |
| ] |
| } |
| }, |
| { |
| "claimIds": [ |
| "CLAIM-HYBRID-PATTERN", |
| "CLAIM-DUAL-OPERATIONS", |
| "CLAIM-RAFT-SCOPE" |
| ], |
| "id": "RULE-HYBRID-CANDIDATE", |
| "kind": "decision", |
| "nextSteps": [ |
| "Test retrieval and behavior adaptation separately before testing the combined system; retain both rollback paths." |
| ], |
| "outcomeId": "HYBRID", |
| "priority": 70, |
| "sourceIds": [ |
| "SOURCE-RAFT-PAPER", |
| "SOURCE-MICROSOFT-RAG-VS-FT" |
| ], |
| "when": { |
| "all": [ |
| { |
| "criterionId": "behavior_change", |
| "operator": "equals", |
| "value": "yes" |
| }, |
| { |
| "criterionId": "representative_examples", |
| "operator": "equals", |
| "value": "yes" |
| }, |
| { |
| "criterionId": "authoritative_corpus", |
| "operator": "equals", |
| "value": "yes" |
| }, |
| { |
| "criterionId": "retrieval_operations", |
| "operator": "equals", |
| "value": "yes" |
| }, |
| { |
| "criterionId": "training_operations", |
| "operator": "equals", |
| "value": "yes" |
| }, |
| { |
| "criterionId": "stable_narrow_task", |
| "operator": "equals", |
| "value": "yes" |
| } |
| ], |
| "any": [ |
| { |
| "criterionId": "fresh_facts", |
| "operator": "equals", |
| "value": "yes" |
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| { |
| "criterionId": "source_citations", |
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| "value": "yes" |
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| } |
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| "CLAIM-RAG-FRESHNESS-FIT", |
| "CLAIM-RAG-PROVENANCE" |
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| "Build a retrieval baseline and evaluate passage relevance, claim support, answer quality, freshness, and recovery from stale indexes." |
| ], |
| "outcomeId": "RAG", |
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| "SOURCE-RAG-PAPER", |
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| { |
| "criterionId": "authoritative_corpus", |
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| { |
| "criterionId": "retrieval_operations", |
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| "any": [ |
| { |
| "criterionId": "fresh_facts", |
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| { |
| "criterionId": "source_citations", |
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| "value": "yes" |
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| } |
| }, |
| { |
| "claimIds": [ |
| "CLAIM-FINE-TUNING-BEHAVIOR", |
| "CLAIM-EXAMPLES-REQUIRED", |
| "CLAIM-LORA-METHOD" |
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| "Compare prompt-only and fine-tuned candidates on target behavior, held-out examples, regressions, and rollback readiness." |
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| "SOURCE-OPENAI-MODEL-OPTIMIZATION", |
| "SOURCE-MICROSOFT-RAG-VS-FT", |
| "SOURCE-LORA-PAPER" |
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| "all": [ |
| { |
| "criterionId": "behavior_change", |
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| "value": "yes" |
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| { |
| "criterionId": "representative_examples", |
| "operator": "equals", |
| "value": "yes" |
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| { |
| "criterionId": "training_operations", |
| "operator": "equals", |
| "value": "yes" |
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| "operator": "equals", |
| "value": "yes" |
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| { |
| "criterionId": "fresh_facts", |
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| { |
| "criterionId": "source_citations", |
| "operator": "equals", |
| "value": "yes" |
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| } |
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| { |
| "claimIds": [ |
| "CLAIM-EVAL-FIRST" |
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| "Measure the current prompt or workflow baseline and document a specific failure class before adding architecture." |
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| "SOURCE-OPENAI-MODEL-OPTIMIZATION" |
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| "all": [ |
| { |
| "criterionId": "fresh_facts", |
| "operator": "equals", |
| "value": "no" |
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| { |
| "criterionId": "source_citations", |
| "operator": "equals", |
| "value": "no" |
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| { |
| "criterionId": "behavior_change", |
| "operator": "equals", |
| "value": "no" |
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| "claimIds": [ |
| "CLAIM-EVAL-FIRST" |
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| "sourceIds": [ |
| "SOURCE-OPENAI-MODEL-OPTIMIZATION", |
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| { |
| "criterionId": "fresh_facts", |
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| "Benchmark BM25 or another lexical path alongside embeddings, then evaluate fusion and reranking on exact-term failures." |
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| "SOURCE-ANTHROPIC-CONTEXTUAL-RETRIEVAL" |
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| "SOURCE-RAG-PAPER", |
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| "all": [ |
| { |
| "criterionId": "source_citations", |
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| "Benchmark end-to-end latency and cost on the intended provider, corpus, traffic shape, and quality target." |
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| "SOURCE-MICROSOFT-RAG-VS-FT", |
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| { |
| "criterionId": "strict_runtime_budget", |
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| "CLAIM-PRIVACY-TOPOLOGY" |
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| "Retain the verified data-flow map and recheck provider retention, training, access, and deletion controls before release." |
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| "sourceIds": [ |
| "SOURCE-OPENAI-DATA-CONTROLS" |
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| "all": [ |
| { |
| "criterionId": "privacy_topology", |
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| } |
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| { |
| "claimIds": [ |
| "CLAIM-PRIVACY-TOPOLOGY" |
| ], |
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| ], |
| "priority": 240, |
| "sourceIds": [ |
| "SOURCE-OPENAI-DATA-CONTROLS", |
| "SOURCE-MICROSOFT-RAG-VS-FT" |
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| { |
| "criterionId": "privacy_topology", |
| "operator": "equals", |
| "value": "no" |
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| { |
| "criterionId": "privacy_topology", |
| "operator": "equals", |
| "value": "unknown" |
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| "CLAIM-HYBRID-PATTERN", |
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| "Version the corpus, index, training dataset, base model, adapter or tuned model, and evaluation suite as separate release inputs." |
| ], |
| "priority": 250, |
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| "SOURCE-RAFT-PAPER" |
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| "criterionId": "retrieval_operations", |
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| "criterionId": "training_operations", |
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| { |
| "criterionId": "fresh_facts", |
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| ], |
| "version": "2026-08-18.1" |
| }, |
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| "scenarios": [ |
| { |
| "candidateOutcome": "RAG", |
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| "CLAIM-RAG-FRESHNESS-FIT", |
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| "sourceIds": [ |
| "SOURCE-RAG-PAPER", |
| "SOURCE-MICROSOFT-RAG-VS-FT" |
| ], |
| "title": "Changing support knowledge base", |
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| }, |
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| "SOURCE-RAFT-PAPER", |
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| "title": "Domain assistant with specialized behavior", |
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| "title": "Architecture request without an evaluation set", |
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| "id": "SOURCE-PHAROS-GUIDE", |
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| "title": "RAG vs Fine-Tuning: When to Use Each for AI Projects", |
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| "updated": "2026-06-29", |
| "url": "https://pharosproduction.com/insights/engineering/rag-vs-fine-tuning/", |
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| { |
| "id": "SOURCE-RAG-PAPER", |
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| "publisher": "arXiv", |
| "title": "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks", |
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| "url": "https://arxiv.org/abs/2005.11401", |
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| "verifiedOn": "2026-08-18" |
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| "id": "SOURCE-MICROSOFT-RAG-VS-FT", |
| "publisher": "Microsoft Learn", |
| "title": "Augment language models with RAG or fine-tuning", |
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| "updated": "2026-01-30", |
| "url": "https://learn.microsoft.com/en-us/azure/developer/ai/augment-llm-rag-fine-tuning", |
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| }, |
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| "id": "SOURCE-ANTHROPIC-CONTEXTUAL-RETRIEVAL", |
| "published": "2024-09-19", |
| "publisher": "Anthropic", |
| "title": "Introducing Contextual Retrieval", |
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| "url": "https://www.anthropic.com/engineering/contextual-retrieval", |
| "useBoundary": "Supports hybrid lexical-semantic retrieval and reranking patterns; reported experiments remain provider-specific.", |
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| { |
| "id": "SOURCE-OPENAI-MODEL-OPTIMIZATION", |
| "publisher": "OpenAI", |
| "title": "Model optimization", |
| "type": "official-documentation", |
| "url": "https://developers.openai.com/api/docs/guides/model-optimization", |
| "useBoundary": "Supports an eval-first optimization loop and fine-tuning with representative examples; product availability can change.", |
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| "id": "SOURCE-LORA-PAPER", |
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| "publisher": "arXiv", |
| "title": "LoRA: Low-Rank Adaptation of Large Language Models", |
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| "url": "https://arxiv.org/abs/2106.09685", |
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| }, |
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| "id": "SOURCE-RAFT-PAPER", |
| "published": "2024-03-15", |
| "publisher": "arXiv", |
| "title": "RAFT: Adapting Language Model to Domain Specific RAG", |
| "type": "research-paper", |
| "url": "https://arxiv.org/abs/2403.10131", |
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| "id": "SOURCE-OPENAI-DATA-CONTROLS", |
| "publisher": "OpenAI", |
| "title": "Data controls in the OpenAI platform", |
| "type": "official-documentation", |
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| "useBoundary": "Used only to show that retention and training treatment depend on service and endpoint configuration.", |
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| "verifiedOn": "2026-08-18", |
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|
|
