Why AI Agents Must Leave Detailed Evidence Trails Beyond Just Git Commits

As AI agents move from answering questions to actively modifying code, configs, and infrastructure, traditional audit tools like Git commits are proving insufficient to capture the full context of their decisions. Unlike human developers, AI agents cannot be questioned after the fact, yet they can call multiple tools, read sensitive files, and open pull requests within seconds before anyone notices. Key missing details include the original user request, the sequence of tool calls, what data the agent read before making changes, and whether a human meaningfully reviewed or merely clicked through a permission prompt. Experts argue that every agent action affecting external state should produce a structured evidence trail covering these dimensions, without necessarily adding heavyweight approval workflows that would render agents impractical. This level of traceability becomes critical months later during production incidents, security audits, or compliance reviews when reconstructing an agent's reasoning chain.