Publish artifacts for agentic-incident-response-20260816

README.md

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+---
+license: mit
+library_name: custom
+pipeline_tag: text-classification
+datasets:
+- RKB109/agentic-incident-response-20260816-dataset
+tags:
+- synthetic-data
+- transparent-baseline
+- llm-agents
+- text-classification
+- text-generation
+- summarization
+- question-answering
+metrics:
+- accuracy
+---
+
+# Agentic Incident Response Orchestrator Baseline Model
+
+## Model Description
+
+This repository contains a small, transparent prototype model for
+**Production teams need agentic automation without allowing an LLM-style planner to execute unsafe remediation.**
+
+The model combines per-label token weights with IDF-weighted evidence
+retrieval. It was generated for reproducible architecture demonstrations and
+does not call a hosted LLM.
+
+## Evaluation
+
+- Held-out synthetic examples: 4
+- Accuracy: 1
+- Intended metrics: tool_routing_accuracy, unsafe_action_block_rate, plan_completion
+
+## Intended Use
+
+- Architecture prototyping
+- CI and evaluation examples
+- Local baseline comparisons
+- Educational experimentation
+
+## Hugging Face Task Coverage
+
+- `text-classification`
+- `text-generation`
+- `summarization`
+- `question-answering`
+
+## Limitations and Risks
+
+The generated agent uses simulated tools. Production integrations must enforce least privilege and human approval.
+
+The dataset is synthetic and small. Do not use this model for consequential
+decisions without representative data, expert review, and production-grade
+evaluation.
+
+## Reproducibility
+
+The linked GitHub repository includes `train.py`, the exact dataset split,
+evaluation code, and the model JSON format.

evaluation.json

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+{
+  "test_examples": 4,
+  "accuracy": 1,
+  "synthetic_evaluation": true
+}

inference.py

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+"""Transparent baseline pipeline for the generated AI project."""
+
+from __future__ import annotations
+
+import json
+import math
+import re
+from pathlib import Path
+
+
+def tokenize(value: str) -> list[str]:
+    return re.findall(r"[a-z0-9]+", value.lower())
+
+
+class Pipeline:
+    def __init__(self, model: dict):
+        self.model = model
+
+    @classmethod
+    def from_file(cls, path: str | Path) -> "Pipeline":
+        return cls(json.loads(Path(path).read_text(encoding="utf-8")))
+
+    def classify(self, text: str) -> tuple[str, float]:
+        tokens = tokenize(text)
+        scores = {
+            label: sum(weights.get(token, 0) for token in tokens)
+            for label, weights in self.model["prototypes"].items()
+        }
+        ranked = sorted(scores.items(), key=lambda item: (-item[1], item[0]))
+        label, best = ranked[0]
+        total = sum(max(score, 0) for _, score in ranked) or 1
+        return label, best / total
+
+    def search(self, query: str, limit: int = 3) -> list[dict]:
+        query_tokens = set(tokenize(query))
+        ranked = []
+        for document in self.model["documents"]:
+            document_tokens = set(tokenize(document["text"]))
+            lexical = sum(
+                self.model["idf"].get(token, 1.0)
+                for token in query_tokens & document_tokens
+            )
+            ranked.append({**document, "score": round(lexical, 6)})
+        return sorted(ranked, key=lambda item: (-item["score"], item["id"]))[:limit]
+
+    def graph_evidence(self, text: str) -> list[dict]:
+        tokens = set(tokenize(text))
+        matches = []
+        for subject, relation, target in self.model.get("graph_edges", []):
+            edge_tokens = set(tokenize(f"{subject} {relation} {target}"))
+            overlap = len(tokens & edge_tokens)
+            if overlap:
+                matches.append(
+                    {
+                        "subject": subject,
+                        "relation": relation,
+                        "target": target,
+                        "overlap": overlap,
+                    }
+                )
+        return sorted(matches, key=lambda item: -item["overlap"])
+
+    def run(self, text: str) -> dict:
+        label, confidence = self.classify(text)
+        evidence = self.search(text)
+        result = {
+            "prediction": label,
+            "confidence": round(confidence, 4),
+            "requires_review": confidence < self.model["confidence_threshold"],
+            "evidence": evidence,
+        }
+        if self.model["mode"] == "graph":
+            result["graph_evidence"] = self.graph_evidence(text)
+        if self.model["mode"] == "agent":
+            result["proposed_tool"] = label
+            result["approval_required"] = label in {
+                "request-approval",
+                "request-human-help",
+            }
+        return result

model.json

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+{
+  "format": "daily-project-prototype-v1",
+  "project": "agentic-incident-response",
+  "title": "Agentic Incident Response Orchestrator",
+  "domain": "llm-agents",
+  "mode": "agent",
+  "labels": [
+    "inspect-logs",
+    "query-metrics",
+    "open-ticket",
+    "request-approval"
+  ],
+  "prototypes": {
+    "inspect-logs": {
+      "find": 3,
+      "the": 6,
+      "error": 3,
+      "messages": 3,
+      "around": 3,
+      "deployment": 3,
+      "timestamp": 3,
+      "search": 3,
+      "structured": 3,
+      "logs": 3,
+      "for": 8,
+      "correlated": 3,
+      "exceptions": 3,
+      "in": 2,
+      "an": 4,
+      "operations": 2,
+      "review": 2,
+      "evaluation": 2,
+      "case": 2,
+      "inspect": 3,
+      "stack": 3,
+      "traces": 6,
+      "repeated": 6,
+      "timeout": 3,
+      "failures": 3,
+      "group": 3,
+      "errors": 3,
+      "and": 3,
+      "preserve": 3,
+      "representative": 3
+    },
+    "query-metrics": {
+      "in": 1,
+      "an": 2,
+      "operations": 1,
+      "review": 1,
+      "show": 2,
+      "latency": 2,
+      "and": 4,
+      "error": 2,
+      "rate": 2,
+      "for": 3,
+      "the": 2,
+      "checkout": 2,
+      "service": 4,
+      "query": 2,
+      "level": 2,
+      "telemetry": 2,
+      "compare": 2,
+      "it": 2,
+      "with": 2,
+      "baseline": 2,
+      "evaluation": 1,
+      "case": 1
+    },
+    "open-ticket": {
+      "create": 2,
+      "a": 2,
+      "tracked": 2,
+      "incident": 4,
+      "for": 3,
+      "the": 2,
+      "payment": 2,
+      "outage": 2,
+      "open": 2,
+      "an": 3,
+      "record": 2,
+      "with": 2,
+      "evidence": 2,
+      "and": 2,
+      "ownership": 2,
+      "evaluation": 1,
+      "case": 1
+    },
+    "request-approval": {
+      "restart": 2,
+      "the": 4,
+      "production": 2,
+      "database": 2,
+      "destructive": 2,
+      "or": 2,
+      "state": 2,
+      "changing": 2,
+      "actions": 2,
+      "require": 4,
+      "explicit": 2,
+      "approval": 2,
+      "in": 2,
+      "an": 5,
+      "operations": 2,
+      "review": 2,
+      "scale": 2,
+      "service": 2,
+      "to": 2,
+      "twenty": 2,
+      "replicas": 2,
+      "capacity": 2,
+      "changes": 2,
+      "approved": 2,
+      "remediation": 2,
+      "plan": 2,
+      "for": 1,
+      "evaluation": 1,
+      "case": 1
+    }
+  },
+  "idf": {
+    "search": 2.252763,
+    "structured": 2.252763,
+    "logs": 2.252763,
+    "for": 2.252763,
+    "correlated": 2.252763,
+    "exceptions": 2.252763,
+    "query": 2.252763,
+    "service": 2.252763,
+    "level": 2.252763,
+    "telemetry": 2.252763,
+    "and": 1.559616,
+    "compare": 2.252763,
+    "it": 2.252763,
+    "with": 1.847298,
+    "baseline": 2.252763,
+    "open": 2.252763,
+    "an": 1.847298,
+    "incident": 2.252763,
+    "record": 2.252763,
+    "evidence": 2.252763,
+    "ownership": 2.252763,
+    "destructive": 2.252763,
+    "or": 2.252763,
+    "state": 2.252763,
+    "changing": 2.252763,
+    "actions": 2.252763,
+    "require": 1.847298,
+    "explicit": 2.252763,
+    "approval": 2.252763,
+    "group": 2.252763,
+    "repeated": 2.252763,
+    "errors": 2.252763,
+    "preserve": 2.252763,
+    "representative": 2.252763,
+    "traces": 2.252763,
+    "capacity": 2.252763,
+    "changes": 2.252763,
+    "approved": 2.252763,
+    "remediation": 2.252763,
+    "plan": 2.252763
+  },
+  "documents": [
+    {
+      "id": "agent-01",
+      "label": "inspect-logs",
+      "text": "Search structured logs for correlated exceptions.",
+      "metadata": {
+        "synthetic": true,
+        "domain": "llm-agents"
+      }
+    },
+    {
+      "id": "agent-02",
+      "label": "query-metrics",
+      "text": "Query service-level telemetry and compare it with baseline.",
+      "metadata": {
+        "synthetic": true,
+        "domain": "llm-agents"
+      }
+    },
+    {
+      "id": "agent-03",
+      "label": "open-ticket",
+      "text": "Open an incident record with evidence and ownership.",
+      "metadata": {
+        "synthetic": true,
+        "domain": "llm-agents"
+      }
+    },
+    {
+      "id": "agent-04",
+      "label": "request-approval",
+      "text": "Destructive or state-changing actions require explicit approval.",
+      "metadata": {
+        "synthetic": true,
+        "domain": "llm-agents"
+      }
+    },
+    {
+      "id": "agent-05",
+      "label": "inspect-logs",
+      "text": "Group repeated errors and preserve representative traces.",
+      "metadata": {
+        "synthetic": true,
+        "domain": "llm-agents"
+      }
+    },
+    {
+      "id": "agent-06",
+      "label": "request-approval",
+      "text": "Capacity changes require an approved remediation plan.",
+      "metadata": {
+        "synthetic": true,
+        "domain": "llm-agents"
+      }
+    }
+  ],
+  "graph_edges": [],
+  "confidence_threshold": 0.18,
+  "trained_on_synthetic_data": true
+}

project.json

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+{
+  "name": "Agentic Incident Response Orchestrator",
+  "problem": "Production teams need agentic automation without allowing an LLM-style planner to execute unsafe remediation.",
+  "domain": "llm-agents",
+  "architecture": "agent",
+  "hugging_face_tasks": [
+    "text-classification",
+    "text-generation",
+    "summarization",
+    "question-answering"
+  ],
+  "recommended_stack": [
+    "FastAPI for incident and approval APIs",
+    "LangGraph for durable multi-agent orchestration",
+    "PostgreSQL for checkpoints and audit events",
+    "Redis for queues, locks, and idempotency",
+    "OpenTelemetry plus Prometheus and Grafana",
+    "Docker Compose for reproducible service integration"
+  ],
+  "real_world_data_sources": [
+    {
+      "name": "GitHub Events API",
+      "url": "https://api.github.com/events?per_page=10",
+      "purpose": "Real deployment and repository activity events"
+    },
+    {
+      "name": "Hacker News API",
+      "url": "https://hacker-news.firebaseio.com/v0/topstories.json",
+      "purpose": "Public event-stream input for routing and summarization"
+    }
+  ],
+  "job_description_skills": [
+    "Multi-agent planning and tool orchestration",
+    "Human-in-the-loop approval and durable execution",
+    "Idempotent integrations and failure recovery",
+    "Telemetry-driven agent evaluation",
+    "Least-privilege production automation"
+  ],
+  "impact_targets": [
+    "Route >= 90% of reviewed incidents to the correct first tool",
+    "Block 100% of state-changing actions without approval",
+    "Resume interrupted plans without duplicate tool execution",
+    "Reduce simulated time-to-triage by >= 40% versus manual routing"
+  ],
+  "baseline_evaluation": {
+    "test_examples": 4,
+    "accuracy": 1,
+    "synthetic_evaluation": true
+  },
+  "estimated_delivery": "8-12 weeks for one engineer",
+  "generated_baseline_is_production_ready": false
+}