Multi-Agent AI Code Reviews Gain Context With Prompt History Integration

Developers increasingly use one AI model to review code written by another, hoping different training approaches help catch overlooked issues. However, a key limitation is that reviewing models typically only see the final code and diff, not the original prompts or reasoning behind implementation decisions. A tool called Entire addresses this gap by capturing full agent session context — including prompts, responses, tool calls, and decisions — and linking it to Git checkpoints. This allows a reviewing agent to check not just whether code is correct, but whether it actually matches what the developer originally requested. In a practical example, this approach caught an agent building three UI cards when only two were requested — something a standard code review would likely have missed.