Insight

How to add AI workflows without losing control.

AI workflows become usable when the model is only one layer in a larger system: retrieval, permissions, review, fallback behavior, and monitoring all have to be designed intentionally.

See AI workflow development Talk about an AI workflow

1. Start with a bounded use case

The workflow should have a clear business objective, clear inputs, and an obvious failure mode before any model choice matters.

2. Add review and fallback paths

Human approval, escalation, retry logic, and safe fallback states are what make AI workflows trustworthy in operations.

3. Monitor quality continuously

Latency, cost, retrieval quality, and operational outcomes should be observable enough to improve the workflow without guesswork.

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