Platform

Audit

One trail across the platform for people and AI employees alike, carrying the actor, the operation, the outcome and - for model calls - the model, prompt hash, tokens and cost

One trail, both kinds of actor

People and AI employees are recorded on the same trail, under their own identities. "Who changed this in March" is answerable without first establishing whether the answer is a person.

What is recorded

Source Examples
Platform operations Access changes, installs, configuration, administrative actions
Connector calls Every operation invoked from a workflow, with its actor and outcome
Agent decisions Every tool call an agent makes, in chat and in background execution
Denials A refused tool call, with a reason code distinguishing a guardrail block from a refused approval
Model calls Model id, prompt content hash, token counts, cost, latency, attempts

The last two rows are what make an agent's behaviour investigable and not merely visible. See The decision trace.

Redaction before persistence

Arguments and output pass through a redaction pipeline on the way to the trail, so the record of an action does not itself become a store of personal data.

Scope and access

The trail is per organisation, like everything else in the platform - see Tenant isolation - and reading it is an ordinary platform permission, not a developer-only surface.

The activity stream is a different record

The audit trail records actions. The record of what changed on an object over time is the object's own activity stream, alongside the discussion about it - see Collaboration. The two answer different questions and are stored separately.