See every agent. Trace every decision. Control every action.
A growing AI workforce needs more than logs after the fact. Govern the fleet, inspect how work gets done, and keep people in control of consequential decisions.
The whole fleet and the full story behind every action.
Start with the fleet-wide picture, then follow any run down to its evidence, decisions, policy checks, human interventions, and final outcome.
Which agents need attention?
Agent identity, workflow, state, and exceptions in one view.
- 01 / EvidenceSource records and context
- 02 / DecisionApplied rules and model lineage
- 03 / ControlPolicy checks and evaluations
- 04 / ApprovalHuman review when required
- 05 / OutcomeAction, write-back, and audit
When should a human step in?
Approval gates, review queues, and escalation with the case context attached.
Visibility, accountability, and intervention at every layer.
Agentic AI governance combines the operating view with enforceable controls: who an agent is, what it can do, how decisions are reviewed, and whether quality holds over time.
Fleet Overview
See which agents are running, what they are working on, and where exceptions or intervention need attention across workflows and teams.
Decision Trail
Trace a decision from its underlying evidence through the rules, model, approvals, and action taken. Make the work inspectable, not opaque.
Human Oversight
Set approval thresholds, route exceptions to the right reviewer, and preserve the reviewer's decision alongside the agent's work.
Compliance
Connect obligations and operating policies to each workflow, with evidence-linked records that support audit and review across jurisdictions.
Guardrails
Apply permissions and policy checks before actions execute; scope tools by role, stop unsafe work, and suspend agents when needed.
Evaluations
Score quality, policy adherence, and regressions against representative work before and after agents or models change.
Govern the models behind your agents, too.
Keep a clear view of which models serve which agents, what each is approved for, and how it performs. Choose the right model for the task without locking the workforce to one provider.
Approved choices
Catalog the models available to each workflow, including frontier, open-source, and customer-owned models.
Policy-based routing
Apply task, data boundary, and approval requirements when selecting or changing a model.
Comparable evidence
Keep evaluations and decision lineage attached as models change, so governance travels with the work.
Learn more about security and runtime controls.
Move fast without losing control.
Make agents accountable to your people, policies, and outcomes — from the first workflow to the whole enterprise fleet.