Operating evidence guide
How to see what your AI workflow did
Keep a work history that connects technical activity to the business task. Record the input, identity, rules and model versions, tools used, approvals, result identifiers, timing, and outcome. Alert someone when there is a problem they can resolve, such as a stalled approval, repeated failure, or incorrect result.
- Good fit
- Use this model when an agent or automation spans several systems and operators need to answer what happened, why it happened, whether it completed, and how to recover.
- Pause when
- Raw model traces and infrastructure logs are not sufficient if they omit customer-safe identifiers, business state, decision ownership, external side effects, or the runbook action expected from an alert.
When this approach helps
- A tool reports success but the expected external record, publication, or customer state is missing.
- Operators receive alerts without enough context or authority to resolve the underlying case.
- The team cannot compare failures by workflow version, provider, policy, tenant, or outcome class.
Follow these steps
- 01
Define the record you need
Choose identifiers and statuses that show the task from start to result. Include source links and dates. Minimize sensitive data and restrict access by role.
- 02
Record decisions and failures
Record model, prompt, policy, tool, and data-schema versions. Include access checks, approvals, retries, errors, and reasons for stopping. Distinguish inferred content from source facts.
- 03
Check that external changes happened
After a write, check the external identifier or status when practical. Keep queued, pending, draft, published, and reconciled results separate. A successful request may not mean the work is finished.
- 04
Send alerts someone can act on
Group alerts by impact, person responsible, urgency, and recovery action. Test dashboards and instructions with failed cases. Use the patterns to improve the system.
Documents and records to keep
Workflow receipt schema
A structured record connects case, source, identity, decision, policy, tools, approvals, external results, timestamps, privacy class, and final status.
Operator exception view
The view groups unresolved cases by owner and recovery action, shows source-linked evidence, and distinguishes retryable failures from decisions that need human judgment.
Outcome reconciliation job
A bounded check compares claimed completion with the authoritative external state and produces a correction, escalation, or verified-close receipt.
Common questions
What is the difference between AI tracing and workflow observability?
Tracing explains model and tool execution. Workflow observability adds source identity, business state, policy, approval, external outcomes, ownership, and recovery so an operator can resolve the case rather than only inspect a technical span.
What should an AI workflow alert contain?
Include the case and tenant identifiers, affected business state, severity, safe evidence summary, likely boundary, responsible owner, allowed recovery action, and links to the receipt and runbook. Never place secrets in the alert.
How long should AI workflow evidence be retained?
Set retention by business, contractual, legal, privacy, and recovery needs for each evidence class. Keep identifiers and decision proof only as long as justified, restrict access, support deletion, and avoid retaining full sensitive prompts by default.