Hey PH — I'm Mauricio, the maker. I built KDD because the part of AI-assisted coding I trust least is the "done, tests pass" moment: the verification is as non-deterministic as the agent. A human reviews the diff, or the same model that wrote the code also grades it. Both can be fooled. What's different: the agent that implements is never the one that decides "this is good." You author the tests before delegating and seal their SHA256 into the contract. If the implementer rewrites a test to make it pass, the hash mismatches and a deterministic gate catches it — no LLM judgment involved. The whole verification path is 12 gates in pure Python stdlib, no network, no LLM, exposed as an MCP server with 14 tools and as reusable GitHub Actions. The feedback I'd most like: does the frozen-test model actually hold once the tests themselves need to evolve? And is the upfront cost of writing a contract before each task worth it for your team, or only for the risky ones? Happy to go deep in the comments.

KDD

Govern AI agents with deterministic gates. No LLM judging.

Govern AI agents with deterministic gates. No LLM judging.