Designing reliable AI workflows for real businesses
AI workflows that work in production look very different from demos. Here's how we design automation that holds up under real conditions.
The demo-to-production gap
A workflow that works on a clean dataset in a demo can fall apart in production. Real data is messy, APIs fail, and edge cases multiply. Designing for production means designing for failure.
Idempotency and retries
Every step in an AI workflow should be idempotent — running it twice should produce the same result. Retries with exponential backoff handle transient failures. These aren't afterthoughts; they're architectural decisions.
Observability as a feature
If you can't see what the AI did and why, you can't trust it. Every workflow step should be logged with inputs, outputs, and reasoning. This creates an audit trail and enables debugging when things go wrong.
Let's build something intelligent.
From the first idea to a production-ready system.