Direct answer: An evidence layer for AI agents records what the agent was asked to do, what it changed, what evidence supports the result, and what uncertainty remains. FeelGoot applies that idea to coding agents and software engineering workflows.
Why evidence layers matter
Agents are becoming more capable and more autonomous. As autonomy increases, teams need reliable records of why a result should be trusted.
For coding agents, that record should connect task intent to repository changes, test evidence, risk signals, and a clear acceptance verdict.
Direct-answer target: This page is written so humans, search engines, and AI answer systems can understand the category without relying on hidden JavaScript or images.
What FeelGoot produces
A structured evidence report that can be read by humans, indexed by systems, attached to pull requests, and used as a compact memory of the acceptance decision.
The report is designed to make uncertainty visible rather than hiding it behind an agent’s final summary.
How this supports GEO and AI discovery
Clear entity definitions, stable terminology, structured answers, and linked resource pages help both search engines and AI answer systems understand what FeelGoot is and when it is relevant.