Agent Incident Recorder (A.I.R.)

Truth preservation in agentic workflows.

When an AI agent executes a sequence of actions, A.I.R. records each transition and validates it against an explicit finite-state workflow. No ML. No guessing. No hallucinated reconstruction of what should have happened.

The goal is not to predict intent. The goal is to preserve operational truth.

What It Does

• Records every agent action as a state transition.
• Validates transitions against authorized workflows.
• Flags out-of-sequence execution drift.
• Generates forensic incident reports on violations.
• Runs as an observer with no control-plane authority.

What It Does Not Do

• Predict failure.
• Train models.
• Parse natural language.
• Score probabilistic risk.
• Modify agent behavior at runtime.

Use Case

A.I.R. is designed for compliance-critical agent deployments in domains such as finance, healthcare, supply chain operations, and regulated infrastructure, where an audit trail is as important as the final result.

Core Architecture

• Input: Agents POST a JSON payload representing state transitions after each execution step.
• Validation: The system checks each transition against hardcoded, authorized workflow definitions.
• Incident Record: Unauthorized transitions produce a forensic incident report for review and replay.

Status

Core logic is in prototype form and ready for evaluation.

Next priorities:

• Integration harnesses for common agent runtimes.
• Forensic export formats.
• Example workflows and violation fixtures.