Give Your Coding Agents Proof Obligations, Not Instructions
Six AI agents wrote a complete screen of my cockpit in two hours. That is not the interesting part. TL;DR : code generation is no longer the bottleneck. Proof is. A multi-agent workflow is only worth its verification harness. Mine demands four proofs: visual parity with the design, flows clicked in a real browser, green tests, an empty punch-list. Give your agents proof obligations. Not longer…
A multi-agent workflow that generates code can produce a screen in a few hours, but the real challenge is not the speed of generation. The bottleneck lies in verifying the output. To ensure a coding agent creates reliable software, it is essential to give it proof obligations instead of free instructions. In this setup, a personal Go app with full-screen components like list, detail view, creation, and status edition was created.
Six specialized AI agents were orchestrated to handle backend, design, verification, and testing tasks. The proof obligations required four checks: visual parity with the design, real-browser flow verification, passing green tests, and an empty punch-list. The visual parity check compared the agent's rendering against a reference mockup, which had to be approved.
Real browser verification ensured the flows clicked in a real app were tested, while the tests gate required a green suite. The environment was enforced through written rules, such as idempotent seeding and Docker Compose restarts. Before launching an agent workflow, it is crucial to ensure an enforceable reference, binary proof obligations, visual verification, real-browser flow testing, green tests as a gate, and the environment set by written rules.
These practices help separate a production tool from a demo generator and ensure trust in the generated code.
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