AI Agents Are Not Ready for Production (With One Exception)
The hype is ahead of the reliability. But there's one pattern where agents actually work today.
Every demo shows an AI agent browsing the web, booking flights, and writing code autonomously. In production, they break on step 3 of a 10-step workflow and you have no idea why.
Why agents fail in production
- Error compounding. Each step has a 90-95% success rate. Over 10 steps, that's 35-60% end-to-end success. Not good enough.
- No undo. When an agent sends the wrong email or updates the wrong record, there's no rollback. The damage is done.
- Debugging is a nightmare. Agent traces are long, non-deterministic, and hard to reproduce. Good luck explaining to a client why the agent did what it did.
- Cost unpredictability. Agents that loop or retry can burn through API credits fast. We've seen $200 bills from a single stuck agent.
The one exception: structured extraction agents
There's one pattern where agents work reliably in production: when the task is narrow, the tools are limited, and the output is structured.
Example: our document processing pipeline. The agent gets a document, extracts specific fields, validates against a schema, and outputs structured JSON. The tool set is fixed (OCR, extraction, validation). The output is checkable. Failures are catchable.
Our rules for production agents
- Maximum 3-4 tool calls per task
- Every output must be validated against a schema
- Human-in-the-loop for any action with side effects
- Cost caps per agent execution
- Full logging with replay capability
When will agents be ready?
When models are reliable enough for 99.5%+ per-step accuracy. We're not there yet, but the trajectory is clear. Build the infrastructure now, deploy cautiously.