标签：#gatekeeper

github.com/deghosal-2026/agent-tooltrust · pip install agent-tooltrust · field test report · design decisions My last three projects taught me the same thing. Mock agents lie. Unit tests pass. Demos look clean. Then real agents run and everything breaks. On my eval harness, I admitted it: field testing "got added ad hoc, late in the build, because I started getting nervous that unit tests and mock agents were hiding real integration problems." On my observability tool: "I thought it was a detector problem. I was wrong." Same lesson. Three times. But lessons only matter if you change what you do next. So this time I did the opposite. Zero mock agents. 83 real ones across 10 frameworks. A covering design that cut a 12-day test matrix into one afternoon. And a release gate that said: no ship until real agents prove the policy works. It worked. 2,490 tests green. 83/83 agents passed. PyPI published. Repo public. And the 7 failures taught me something I couldn't have learned any other way. The Problem With Allow-Lists Everyone is racing to give AI agents more tools. Almost no one is building the permission system that decides when those tools should fire. Right now, agent permissions are binary: allowed or denied. That's reachability, not authorization. The same tool is harmless in staging and dangerous in production. The same read is fine on public docs and risky on customer data. A delete in a CI sandbox is not the same as delete in production. About 18% of MCP server deployments implement any access scoping. 80% of orgs admit agents have taken actions beyond intended scope. OWASP classifies agent tool misuse as a first-class risk. Giving an agent tools is the easy part. The hard part is deciding what it should be allowed to do, where, and under what guardrails. I wrote a PRD and architecture spec before touching engine code — partly to keep myself honest, partly because I've learned the hard way that skipping design leads to shipping the wrong thing. What I Built Agen