What Nobody Tells You About Running AI Agents in Production

The pattern repeats at nearly every company that has shipped an AI automation: the demo is convincing, the pilot is promising, and the production deployment looks fine for the first two weeks. Then something quiet goes wrong. The agent starts producing outputs that differ from what you expected. A document processing run fails silently. Costs are 40% higher than the estimate. Nobody has a runbook for any of it.

The gap between a demo that works and an automation that runs reliably in production is not primarily a model quality problem. It's an infrastructure problem — and most teams underestimate it until they're already in it.

Error Handling Is Not Optional

LLM APIs fail. They return 500s at peak times, time out on large inputs, and hit rate limits at exactly the wrong moment. Most demo-stage agents have no retry logic, no fallback behavior, and no alerting when they fail silently. The first sign of a problem is often a business user noticing hours later that a batch of documents was never processed.

Robust error handling for an AI automation looks like any other distributed system: exponential backoff on transient failures, a dead-letter queue for inputs the agent can't process, and alerting that fires before the impact reaches the business. The code is straightforward. The decision to build it before you need it is the harder part. In practice, we almost never see this in place on the first version of an in-house AI automation — and we almost always add it within the first month.

Observability: You Can't Debug What You Can't See

When a traditional service behaves unexpectedly, you check the logs and find the stack trace. When an AI agent produces wrong output, the logs show a successful API call and a valid JSON response. The agent did what it was told; the problem is in what it was told, or what the model decided to do with it.

Useful observability for an AI automation means logging the full prompt (including context injected at runtime), the complete model response before parsing, the token count and cost per call, and the final output after post-processing. Without all four, debugging a quality regression becomes archaeology. With them, you can see exactly when and why the agent started producing different outputs — a model version update, a change in input format, a prompt that works with short documents but breaks on long ones. One client of ours had an extraction pipeline that started producing malformed JSON intermittently after a model update. Without the raw response logged, it would have taken days to find.

When Costs Spiral

In a test environment you process 200 documents. In production you process 8,000 per month, some of them 40-page contracts that consume 15,000 tokens each. Token costs don't scale linearly with document volume — they scale with the combination of volume, document size, retry rate, and prompt verbosity. A cost estimate based on test data can be off by 3× or more.

The fix is rarely a better model — it's better prompts and better architecture. Caching the results of identical requests, chunking large documents rather than passing them whole, restructuring prompts to be explicit rather than conversational, routing simple cases to cheaper models. We typically find 40–60% cost reduction potential in the first pass of optimization. The team that built the automation almost always finds it too, once they have per-call cost data to look at. The data is the prerequisite; without it, there's nothing to optimize against.

The 3% the Agent Gets Wrong

Every AI automation has a failure rate. 97% accuracy sounds acceptable until you're processing financial documents, medical records, or contracts with legal weight. The question isn't whether the agent will fail — it's what happens when it does.

Most automations are not designed to answer that question. The failing case either passes into the next system undetected, gets dropped, or triggers a generic error that nobody has a process for. The right design asks: what does the agent need to surface to a human? What information does that human need to correct it? How do corrections flow back to improve the system over time? Building that escalation path is harder than building the agent itself. It's also what makes the difference between an automation that runs and one you actually trust with production data.

AI agents that work reliably in production aren't fundamentally different from agents that work in demos — but they're surrounded by infrastructure the demo didn't need. Monitoring, retry logic, cost accounting, human escalation paths. Teams that build that infrastructure before they need it spend their Friday evenings on other things.

Rebooted Solutions architects and builds AI automations designed for production — from the first pipeline to the observability layer and escalation flows. If your automation is running but not reliably, or you're about to move a pilot to production and want to get the infrastructure right the first time, get in touch.