· Personal Goals Pursuit · Technical · 1 min read
Event driven thinking for reliable AI workflows
AI workflows become safer when you treat them like real systems with state, failure paths, and accountability.
Many AI workflows start as a chain of prompts. That is fine for exploration. It fails in production when the system needs to handle retries, partial failures, and audit questions.
If a workflow matters, it needs design.
Event driven thinking is one way to do that. It is not a technology choice. It is a way of modelling work.
The shift
Instead of thinking in prompts, think in events.
An event is a record that something happened. It can be stored, replayed, and traced.
Examples:
- Request received
- Source data fetched
- Draft produced
- Review passed
- Published
Each step consumes an event and produces the next event. That gives you:
- visibility
- retries without chaos
- clearer ownership per step
Failure paths become manageable
When a step fails, you can:
- retry the step
- route it for human review
- compensate with a follow up action
The key point is that failure is designed, not ignored.
Outcome
Teams that model AI workflows this way ship systems that are easier to debug, safer to evolve, and more predictable.
If you are moving an AI workflow from demo to production, start by listing the events and the failure paths.
