Guide
A practical guide to governed AI delivery.
AI agents become useful in production when teams can control context, execution, validation, review, and delivery. This guide outlines the operating model FlowAI is built around.
Start with the delivery contract
Define what the agentic workflow is allowed to do, what evidence it must produce, and where a human must approve the result.
- State the expected artifact set before execution starts.
- Separate implementation, review, and validation responsibilities.
- Require traceable decisions for approvals and overrides.
Treat validation as part of delivery
Validation should inspect the actual outputs and upstream context, not only a summary. Failed validation should trigger remediation or review.
- Validate structure, engineering quality, and grounded truth.
- Persist evidence so retries can be audited.
- Escalate uncertain outcomes through human review.
Connect automation to operating controls
A production AI workflow needs tenant boundaries, usage metering, secrets management, and external side-effect controls.
- Keep secrets out of generated artifacts and database backups.
- Meter LLM and workflow usage for cost visibility.
- Use idempotency and audit records around external actions.