#845 · Primary category: AI Agents & Automation

agent-safe-pipeline

agentic-ai ai-agent-permissions ai-agents ai-governance ai-safety authorization decionis execution-authority human-in-the-loop mcp policy-as-code reference-architecture typescript

Reference architecture for AI agents that propose actions but cannot authorize them — immutable intent capture, an independent Decionis policy verdict (ALLOW/ESCALATE/BLOCK), verified human approval, and a SafeExecutor that consumes a single-use intent-bound grant.

Project last updated:08/24/26

GitHub Stars

530

Forks

58

Contributors

5

License

Apache-2.0

Why we included this project

Agents usually get to judge whether their own actions are safe; this project is built on the opposite idea. It is a reference architecture and library that separates proposing from authorizing: the agent's intended action is captured as an immutable, hashed intent, an independent Decionis policy service returns an ALLOW, ESCALATE, or BLOCK verdict, and a SafeExecutor runs only the actions that match a single-use, intent-bound grant. Human approval goes through a separate verified path that the policy engine re-checks, so autonomy does not have to mean handing the model the keys. The repo ships runnable examples covering refunds, deploys, and procurement, plus an MCP tool gate and threat-model docs. It is a library, not a hosted service: production use still calls for provider-side identity, least privilege, network isolation, and incident response.

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