Building AI Agents That Run in the Background Without Breaking

An AI agent that works in a demo and an agent that runs reliably in production are very different things. Here's what the gap looks like and how to close it.

The demo trap

AI agents fail for the same reasons software always fails — but faster. They're stateful, dependent on external APIs, and dealing with unpredictable inputs. A demo works because the input is controlled and you're watching it run. Production is none of those things.

Error handling isn't optional

Every API call your agent makes can fail. Rate limits, timeouts, unexpected response formats, API deprecations — all of these will happen in production. Your agent needs to handle them gracefully: log the error, retry where appropriate, and alert someone when it can't continue.

State management is the hardest part

Background agents need to know where they left off if they crash and restart. Design your state persistence before you build the agent logic. A simple database table or Redis store that tracks job status will save you hours of debugging later.

Observability is non-negotiable

You can't debug an agent you can't observe. Log inputs, outputs, intermediate states, and errors. If your agent processes 1,000 items and 47 fail silently, you need to know which 47 and why.

Run at lower velocity first

Start with a throttled, monitored version of your agent before you let it run at full speed. You'll catch the edge cases that break your assumptions before they become a production incident.