Direct answer: For platform teams, FeelGoot provides a verification boundary around AI coding work: it maps intent to changed files, checks evidence quality, flags shortcut risk, and gives reviewers a completion receipt.
The challenge
AI coding agents increase output volume. Without an evidence layer, review queues can become the bottleneck and risky work can look finished too early.
Teams need a consistent acceptance standard that works across agents, repositories, and risk levels.
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How FeelGoot helps
Create a repeatable evidence report for every important agent task.
Separate strong proof from weak proof, blockers, and unknowns.
Give leaders and reviewers a consistent language for agent risk: intent drift, fake-green tests, shortcut implementation, and risky file impact.
Outcome
The team can adopt coding agents without lowering the bar for acceptance. Reviewers get more leverage, and managers get a clearer record of what was accepted and why.