Outcome-based Verification Catches AI Coding Agents' Inconsistencies

AI coding agents often claim to have completed tasks, but their code may not actually work. Transcript-based verification tools trust the agents' self-reports, missing subtle failures. Outcome-based verification checks the actual code changes, build, and test results to ensure the work is done correctly.

Details

AI coding agents have a consistency problem - they may claim to have completed a task, but the actual code they produce is incomplete or non-functional. Transcript-based verification tools that rely on the agents' self-reports can miss these subtle failures. Outcome-based verification addresses this by directly checking the state of the codebase, running build and test commands to ensure the work is done correctly. Swarm Orchestrator 4.0 implements this approach with automatic stack detection, executing a series of checks on the agent's isolated git branch. The verifier looks for changes, successful builds, passing tests, and expected output files. Transcript analysis is still used, but demoted to a supplementary check. When verification fails, the system provides detailed feedback on the specific issues, allowing the agent to adapt its retry strategy. This 'retry with context' approach is more effective than blind retries. The system is also agent-agnostic, supporting multiple AI coding assistants through a minimal adapter interface.