The production reliability lesson that changed how we build AI workflows

AI agents in production need to deliver consistent results with one shot at getting the answer right. Unlike ChatGPT conversations where you can refine and iterate, production agents don’t get do-overs. This creates a temptation to pack everything into a single, comprehensive prompt – after all, isn’t that the promise of AI?

In practice, complex prompts produce volatile results. We learned this the hard way when a client’s customer feedback system worked perfectly in testing but failed randomly in production. We tried to make one agent analyze user stories, provide subtle personalized feedback, and push detailed analysis to backend systems simultaneously – too much complexity in a single prompt.

Our approach now: 

• • Break problems into chunk-sized tasks, then refine simpler prompts to produce reliable outputs. 
• • Chain these focused agents together rather than building one super-agent. 

Yes, it requires more planning upfront, but it leads to dramatically better results and makes debugging possible.

When a feedback analysis fails now, we can instantly identify whether the issue is in story comprehension, response generation, or data extraction for backend processing. With a complex agent, failures were mysteries that took hours to diagnose. 

Simple, chained agents might seem less impressive than AI magic, but they work consistently in real business conditions.

The best AI implementations aren’t the most sophisticated- they’re the most reliable.

Have you experienced reliability issues with complex AI implementations? How do you balance capability with consistency in your automation projects?