README.md

metadata

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.