Compliance-first AI: proving agent provenance for regulated engineering teams

In regulated environments, the challenge of adopting AI agents for code development lies not in their ability to write code but in proving the context they accessed to make changes. The emphasis is on creating a comprehensive audit trail that shows exactly which files an AI agent read and the reason behind its actions, to ensure accountability and compliance. This is crucial for auditing processes, where accuracy and completeness of evidence are paramount. The Model Context Protocol and agentic tooling have enabled AI to access live repositories, but the focus is now on ensuring transparent, scoped retrieval to provide traceable evidence of an agent's actions. Tools like Sourcegraph's Deep Search offer a solution by generating explicit records of sources consulted by AI agents, turning them into audit-ready workflows. This approach not only satisfies control requirements but also streamlines audits by providing a clear, traceable line of reasoning for changes, which is crucial for compliance in sectors like banking, healthcare, and software development.

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