Agent Runtime
A governed runtime for AI actions: task identifiers, approvals, audit trails, and safe re-runs — designed for real operations, not demos.
Execution model
AI tasks as first-class records
Every agent action is represented as a task with an identifier (`ai_task_id`), status history, and ownership.
- ✓Traceable runs
- ✓Review queues
- ✓Clear status transitions
Approvals as an explicit step
High-impact actions are blocked until the right human approves. No silent spending or commitments.
- ✓Human-in-the-loop
- ✓Policy-driven routing
- ✓Audit-ready decisions
Safe re-runs
When something fails, rerun deterministically. Avoid duplicates and preserve history for incident review.
- ✓Deterministic behavior
- ✓Idempotency patterns
- ✓No manual cleanup as a plan
Operational safety
Permissioned actions (RBAC)
Agents act under permissions. Least privilege stays enforceable as teams and workflows expand.
- ✓Scoped permissions
- ✓Segregation of duties
- ✓Fail-closed authorization
Audit trails with evidence
Keep the evidence connected: documents, references, and approvals link back to the action chain.
- ✓Evidence-first approvals
- ✓Searchable history
- ✓Explainable outcomes
Integrations observability
Sync logs are part of operations. Failures and retries remain visible and tenant-scoped.
- ✓Sync status history
- ✓Structured errors
- ✓Safe retry paths
Related
Follow the runtime and governance surface into specific product workflows.
FAQ
Clear answers for teams evaluating governance and runtime design.
Ready to deploy agents with controls?
Start a 14-day trial. No credit card required.
No credit card required. Cancel anytime.