The value is the shape of the output: not advice in prose but a decision, a confidence and a rationale, kept in a hash-chained trail — which is what makes it readable months later by someone asking why the agent proceeded.
A governance gate an agent can call on itself. A proposal is scored on six dimensions — Risk, Profit, Novelty, Complexity, Quality, Utility — and comes back as a structured decision with confidence and rationale.
- A proposal evaluated into PROCEED, PAUSE, HALT or ESCALATE, with confidence scores and rationale
- A fast risk check for when a full evaluation is more than the moment needs
- The thresholds a decision is being measured against
- The scoring guide, so the six dimensions are applied the same way each time
- A hash-chained audit trail behind the decisions
- An audit record written against NIST AI RMF and EU AI Act Annex IV
A recent Python and pip. It runs locally in sandbox mode with no signup; an Aegis API key in the server's environment unlocks decision history, usage reports and risk checks, and a hosted streamable-HTTP endpoint carries the wider tool surface.
One command plus a key — pip install "aegis-governance[mcp]", then supply credentials