Definition
Agent audit log
An agent audit log is an immutable, append-only record of everything an AI agent did and proposed — each action, who or what approved it, when, and against which records — written to the same trail as human actions. It turns 'the agent did something' into an accountable, replayable history, and is what makes governed autonomy verifiable rather than merely promised.
Key takeaways
- An agent audit log is an immutable, append-only record of every agent action and proposal, with who approved it and when.
- It makes governed autonomy verifiable — evidence the agent can't edit, not a story it tells about itself.
- Best practice unifies agent and human actions in one trail so changes are reconstructable and attribution is clean.
- Planoda writes even auto-approved agent proposals to the same audit trail as human edits.
When agents take real actions, the after-the-fact question is always the same: what exactly happened, and was it sanctioned? An agent audit log answers it by recording every consequential operation as an attributed, timestamped, immutable row — including the proposals an agent made, whether they were auto-approved or held for a human, and the decision that resolved them. Because the entries can't be edited, the log is evidence, not a narrative the agent controls.
The key design choice is to put agent actions in the same audit trail as human ones rather than a separate 'AI log.' A single, unified history means an investigator reconstructs a change without stitching two systems together, attribution distinguishes principals cleanly, and the same retention and access controls apply to both. This is also what regulatory directions like the EU AI Act reward — traceability that doesn't depend on trusting the agent's own account.
Planoda writes agent proposals and approvals — even auto-approved ones — to the same audit trail as human edits via an `agent.proposal.*` row from the broker, so an agent's behavior is reviewable end to end and indistinguishable in rigor from a human's.
Related terms
- Audit TrailAn audit trail is an append-only, time-ordered record of who did what, when, and to which object across a system. Every create, edit, delete, and approval is logged immutably, so any state can be traced back to the actions that produced it. Audit trails underpin accountability, debugging, compliance, and — increasingly — oversight of what AI agents do.
- Agent governanceAgent governance is the set of controls that make an AI agent's actions safe, attributable, and reviewable: human approval gates on consequential actions, an immutable audit trail of who approved what, role-based capability limits, and spend controls. It is the difference between an agent that suggests and one you can trust to act.
- Propose / Approve (AI Governance)Propose/approve is a governance pattern for autonomous software: instead of executing a consequential action directly, an AI agent emits it as a proposal that a human or policy must approve before it runs. It keeps fast, read-only work autonomous while gating destructive or irreversible operations — the practical way to give agents real power without surrendering control.
- Agent broker (propose/approve broker)An agent broker is the runtime component that sits between an AI agent and the actions it wants to take, deciding per call whether to execute inline, require human approval, or refuse. It is where propose/approve governance is actually enforced — the gate is code in the request path, not a guideline in a prompt — so an agent literally cannot run a destructive tool without passing through it.
- EU AI ActThe EU AI Act is the European Union's risk-tiered regulation of artificial intelligence. It classifies AI systems by risk and attaches obligations — transparency, human oversight, and traceability — proportional to that risk. For work platforms deploying agents, it pushes toward designs where consequential automated actions are reviewable and attributable by default.