Delivery Integrity Prover Connector for AI agents.

It forces your AI agent to prove it actually finished the work by mapping requirements to specific code changes and providing logs.

It stops the agent from hallucinating success by requiring empirical evidence like test results and file paths before it claims a task is done.

While designed for software development, it works for any task where you need an agent to verify specific steps and admit what's left to do.

No, it forces the agent to provide the logs from the tests it already ran, ensuring the agent doesn't just guess the outcome.

If the agent misses a part of your prompt, the capability will flag it as an incomplete requirement, forcing the agent to go back and fix it.

It creates a structured audit trail. You get a clear summary of what was done, what wasn't, and where the gaps are, all in one place.

AI agents frequently assume that code works without executing it. Requiring actual command output logs forces them to run verification scripts, catching syntax errors and test failures early.

A remaining gap includes any manual check required by the user, edge cases that were explicitly left out of scope, or dependencies on other teams. Banning 'none' forces agents to acknowledge limitations.

It converts simple guidelines into strict capability-call checks. The agent must successfully match requirements to modified code lines and paste actual command outputs to get an approval verdict.