Direct answer: A CI completion gate is an acceptance checkpoint that asks whether AI-generated code has enough evidence to be treated as done. FeelGoot is designed to add that checkpoint near pull requests and CI so teams can allow, block, or request more evidence.
From CI pass to completion evidence
Classic CI answers whether a defined set of jobs passed. AI coding workflows require another question: does the agent’s completion claim match the requested work and the actual evidence?
FeelGoot adds a verification boundary around the claim of done.
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Gate outcomes
Allow: evidence is strong enough for normal human review.
Request evidence: key pieces are missing, weak, or unknown.
Block: the task appears incomplete, misaligned, or shortcut-driven.
Where teams use it
AI-generated pull requests, autonomous coding queues, bug-fix agents, refactoring agents, migration work, release automation, and high-assurance code paths.
The gate is especially useful when many agent-created changes arrive faster than reviewers can manually investigate.