I've been running AI agents in production for five months now. Not toy demos. Actual agents that handle real tasks with real consequences. Here's what I've learned about what breaks, what costs money, and what keeps me up at night.

Lesson 1: Agents Fail Silently

The most dangerous failure mode isn't a crash. It's an agent that completes the task incorrectly and reports success. An agent that updates a database record with wrong data looks identical to one that updates it correctly. Both return "task completed."

My fix: every agent task has a verification step that's independent of the agent itself. If the agent writes to the database, a separate validation query checks the result. If the agent generates a report, a schema validator checks the structure. Trust but verify is the only approach that works.

Lesson 2: Costs Spiral Without Guardrails

My first month's bill was 4x what I expected. The culprit: retry loops. When an agent fails a step, the natural thing to do is retry. But each retry consumes tokens. An agent stuck in a retry loop can burn through $50 of API calls in minutes.

What I implemented:

  • Hard token limits per task. Each agent has a maximum token budget. When it's exhausted, the task fails and alerts a human.
  • Retry limits. Maximum three retries per step, with exponential backoff.
  • Cost alerting. If any single task exceeds $2, it pauses and waits for approval.
  • Daily budget caps. Total agent spending is capped per day. When the cap hits, all non-critical tasks queue until the next day.

Lesson 3: Structured Output Is Non-Negotiable

Free-form text responses from agents are useless in a pipeline. When an agent needs to pass results to the next step, or when you need to parse the output programmatically, you need structured output.

I use JSON schemas for every agent response. The agent must return data in a specific format, and the output is validated against the schema before anything downstream consumes it. If validation fails, the task retries with an explicit "your response didn't match the expected format, here's what was wrong" message.

This single change reduced my pipeline failures by about 40%.

Lesson 4: Context Window Management Is a Real Engineering Problem

My agents process customer support tickets. Some tickets have 50 messages in the thread, plus attachments, plus related tickets. That's way more context than fits in a single API call.

My approach: progressive summarization. The agent first reads a summary of the ticket history (generated by a previous summarization step). If it needs more detail, it requests specific messages. The summary is always included. The full history is on-demand.

This reduced my token usage by 60% while maintaining accuracy because most tasks don't need the full history, just the recent context and key facts.

Lesson 5: Logging Everything Is Cheap, Debugging Without Logs Is Expensive

Every agent call logs: the full prompt, the full response, the token count, the latency, and the validation result. This sounds like overkill until an agent makes a wrong decision and you need to figure out why.

Last month, an agent started miscategorizing support tickets. Without detailed logs, I would have been guessing. With logs, I could see that a recent system prompt change had introduced ambiguity in the categorization rules. Fixed it in five minutes.

I store logs in a structured format (JSON in S3) and keep them for 30 days. The storage cost is negligible compared to the debugging time it saves.

Lesson 6: The Human Fallback Must Be Fast

Every agent task has a human fallback. When the agent can't complete the task, when validation fails, or when confidence is low, it routes to a human. But the fallback path needs to be as well-designed as the happy path.

My first version just sent a Slack message saying "Agent failed, please handle manually." Useless. The human had no context about what was tried, what failed, or what the agent was attempting.

Now, the fallback includes: the original task, what the agent attempted, why it failed, and all relevant context pre-loaded. The human can pick up where the agent left off instead of starting from scratch.

Lesson 7: Start with the Simplest Agent That Could Work

I over-engineered my first agent. Multi-step planning, tool selection, dynamic strategy adjustment. It was impressive in demos and unreliable in production. Too many moving parts meant too many failure modes.

The agents that work best in production are embarrassingly simple. Read input, apply a single transformation, validate output, return. No planning loops, no dynamic tool selection, no multi-step reasoning. When you need complexity, compose simple agents rather than building complex ones.

The Honest Summary

AI agents in production are powerful but demanding. They need the same engineering rigor as any production system: monitoring, alerting, graceful degradation, cost controls, and thorough logging. The AI part is the easy part. The production engineering around it is where the real work lives.

If you're starting out, pick one well-defined task, build the simplest possible agent, add comprehensive validation and logging, and run it for a month before adding the next task. Resist the temptation to build a general-purpose agent framework. You don't need one yet.