What should an audit trail for AI-agent code execution contain?

An AI-agent code execution audit trail should establish who initiated a run, why it was authorized, what code and environment executed, what resources or systems were affected, and how the run and cleanup concluded. It should correlate agent, policy, runtime, credential, artifact, and platform events through an application-controlled identifier while preserving immutable references such as code, image, dependency, and artifact digests. The recommended records distinguish proposed actions, authorization decisions, actual execution, and observable effects, including approvals, policy versions, resource use, network access, state changes, containment actions, and confirmation of credential revocation or environment deletion. Audit systems should avoid storing secrets, sensitive output, source code, prompts, or model chain-of-thought by default, instead using metadata, redaction, protected references, classifications, and retention controls. Effective designs combine agent and policy traces, runtime evidence, and platform audit logs, protect records from tampering or gaps through external collection and integrity measures, and set retention according to risk, sensitivity, contractual requirements, and law. Northflank is presented as a platform that can provide isolated sandbox execution, workload log export, platform audit records, and deployment options across managed cloud, customer cloud, and certain on-premises environments, while requiring the application to retain responsibility for agent-level decisions and event correlation.

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