Traces for AI governance and workflow traceability

Inspect what happened in every AI-assisted workflow: inputs, sources, agent steps, approvals, decisions, outputs, and follow-up actions.

Traceability for governed AI work

Make AI-assisted work inspectable

Traces give people teams, operators, and governance owners a clear record of how work moved through Clous.

Track every run across inputs, sources, tools, approvals, and outputs

See which context and permissions shaped each recommendation

Debug workflow failures without guessing where the chain broke

Create audit-ready evidence for sensitive AI-assisted decisions

Traceability capabilities

Built for governance, debugging, and operational trust.

Run timeline

Inspect each step in an AI run, including source retrieval, agent reasoning boundaries, tool calls, approvals, and outputs.

Source and decision lineage

Connect recommendations back to the documents, records, policies, and workflow rules that informed them.

Governance evidence

Keep review history, owner actions, and exception notes attached to the workflow.

Why traces matter

Teams can adopt AI with clearer control and stronger accountability.

Higher trust

Show why an output was produced and which context influenced it.

Faster debugging

Find bad inputs, stale sources, missing permissions, or workflow gaps quickly.

Stronger governance

Keep audit trails and human review evidence available when decisions are sensitive.

How teams use traces

Start with visibility on one workflow, then standardize governance across the platform.

01

Choose the workflow

Start with a workflow where AI recommendations, approvals, or sensitive data need inspection.

02

Review run evidence

Inspect sources, permissions, outputs, owners, and escalations for each run.

03

Operationalize governance

Use trace findings to improve prompts, sources, approval rules, and operating policies.

Need AI workflows you can inspect?

See how Clous traces make AI-assisted work governable and easier to debug.