AgentKits

Scored against Microsoft's published guidance · AgentAz™ companion

Compliance Scanner

Generated your agent with ChatGPT, Claude, or Cursor? Wrote it by hand? Either way — paste its system prompt or its agentaz.json and see whether it's actually production-safe. Scanned against Microsoft's published agent-governance guidance, with the AgentAz™ companion mapping that shows how each control is met at the prompt layer. Pass/fail gates, failure scenarios, a risk radar, and a copy-paste fix block. Deterministic — same input, same result — and your prompt is processed to produce the score, then discarded: never stored, never sent to a model.

Scored against Microsoft's published agent-governance guidance, with the AgentAz™ companion mapping. Deterministic — same input, same result. Your prompt is processed on our edge to produce the result and is never stored, logged, or sent to any model.

What it scores against

The scanner maps your agent to the design-layer controls in Microsoft's published guidance for AI agents — the Cloud Adoption Framework's governance and security recommendations, the Responsible AI principles, and the agentic maturity model. Each Microsoft control is shown with its AgentAz™ companion: the spec field that satisfies it at the prompt layer.

What it can't see (on purpose)

A system-prompt scan only assesses the design layer. Microsoft's platform-enforced controls — Entra ID identity, Purview data-loss prevention, runtime threat detection — are marked platform, out of scope rather than guessed at. This complements those controls; it doesn't replace them.

Why deterministic, not an LLM

A governance check you can't reproduce isn't a check. The default scan uses fixed rules and the same risky-tool vocabulary the runnable run.py demos enforce, so the same input always produces the same verdict. It never auto-executes or contacts a model.