| --- |
| 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. |
|
|
