Lifecycle and events
Inspect starts, stops, streams, completions, requests, and terminal outcomes.
Run auditing
How do I audit AI agent runs and run history?
Audit AI agent runs by reviewing tenant-scoped run history, request events, schedule markers, outcomes, usage attribution, and redacted operational metrics tied to the runtime that produced them. Bewize Hub provides these operational surfaces for managed AI worker runtimes; audit conclusions should follow recorded evidence rather than unsupported assumptions.
An evidence-led run audit
Start with evidence that explains what ran, where, when, and with what operational result.
Tenant and schedule context
Keep tenant ownership and scheduled-versus-actual run markers visible.
Usage and controls
Review model and token usage, policies, access state, and redacted metrics alongside runs.
Audit references
Observability
Review run history, request events, usage attribution, schedules, and cost-control boundaries.
Learn more →Security
Connect audit evidence to tenant isolation, policies, managed secrets, and access controls.
Learn more →Bewize Hub
See the operator console and control-plane surfaces behind managed agent runs.
Learn more →AI agent run audit FAQ
What should an AI agent audit begin with?
Begin with tenant ownership, run lifecycle, request events, schedules, outcomes, model and token usage, policies, and redacted metrics.
Why does tenant context matter in run history?
Tenant context prevents multiple runtimes from collapsing into one activity stream and keeps investigation tied to the owning boundary.
Does run history prove that every agent action was correct?
No. It provides operational evidence for review; quality and safety conclusions require the relevant evaluation and policy checks.
Build an agent audit path
Discuss run history, schedule evidence, usage attribution, policies, and tenant-scoped investigation.
Request a demo
Share a little context. Our team will reply by email to arrange the next step.