How to Run Custom AI Code Evals on Every Push with Trails
Blog post from Entire
Trails are presented as Entire’s alternative to pull requests for reviewing agent-written code, combining diffs with the coding agent’s captured sessions, checkpoints, timeline, automated review findings, and configurable merge gates. Review agents run independently in cloud sandboxes on matching pushes, assign scores and rationales, and display current results alongside recent runs so teams can assess whether repeated review-and-fix cycles have stabilized. Users can create Trails from branches or planning phases, enable preset agents for code quality, security, and undesirable patterns, or build custom agents such as one that evaluates database migration risks including destructive changes, locking, rollout compatibility, and rollback safety. Trails can span multiple repositories, while GitHub-based repositories may still require linked pull requests and compliance with GitHub branch rules for CI and merging. The workflow emphasizes resolving or assessing findings, pushing updates for rescoring, and merging once review signals are stable and configured requirements are met.
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