Building a demo agent is easy. Building one that runs 40,000 decisions a day without hallucinating, looping, or silently failing — that's a different problem entirely. Here's what we've learned.
We've built and deployed over a dozen production AI agent systems. The pattern is always the same: a demo impresses, a pilot runs cleanly, and then you push to production — and things quietly fall apart. Not in dramatic ways. In the subtle, insidious ways that only show up at scale.
The gap between a working demo and a reliable production system is larger for AI agents than almost any other software category. Here's why, and what we do about it.
Failure mode 1: Hallucination loops
In isolation, a hallucination is a nuisance. In an agent with tool use and multi-step reasoning, it's a loop trigger. An agent that invents a function name calls it, gets an error, reasons about the error incorrectly, invents a fix, and calls again — all while consuming tokens and potentially writing bad state to your database.
The fix isn't prompting alone. You need hard circuit breakers: a maximum step count per task, error-rate monitoring at the tool call level, and a fallback escalation path when the agent exceeds its error budget. We also add explicit "I don't know" outputs for agents that should surface uncertainty rather than guess.
Failure mode 2: Context window overflow
Long-running agents accumulate context. A single agent session handling a complex task can hit 128K tokens without you noticing — until it starts silently truncating the beginning of its memory and losing the original instruction. This is particularly lethal for agents with persistent tasks.
The solution is active context management: summarize and compress intermediate results, maintain a structured working memory separate from the raw conversation thread, and checkpoint progress to external storage. Treat context as a limited resource, not an infinite buffer.
Failure mode 3: Silent tool call failures
When a tool returns an ambiguous result — a 200 OK with an error body, a timeout that resolves eventually, a partial write — the agent often interprets this as success and continues. This is the hardest failure mode to catch because it produces no exception, no log entry, no alert.
Every tool in your agent's arsenal needs a typed response schema with explicit success and failure states. Ambiguity is not allowed. We write tool wrappers that enforce this contract and log every call with its full input/output to a structured observability sink — not just the agent's reasoning trace.
Failure mode 4: No observability
Most teams instrument their APIs but forget their agents. An agent making 40,000 decisions a day is a black box unless you explicitly design for transparency. You need to know: which tasks are completing successfully, where agents are getting stuck, which tools are failing most often, and what the latency distribution looks like per task type.
Treat your agent like a distributed system. It is one. Apply the same observability standards you'd apply to a microservices architecture: structured logging, distributed tracing, anomaly detection, and alerting on business-level metrics — not just HTTP status codes.
Failure mode 5: No regression testing
LLM providers update their models. Prompts that worked with GPT-4-turbo behave differently after a model update. Without a regression suite, you find out about this in production — usually after it's caused real damage.
We maintain an eval harness for every agent we deploy: a set of benchmark tasks with expected outputs (or expected output properties), run automatically on every deployment and on a nightly schedule against the live model. If accuracy drops more than 2% on the benchmark suite, we halt and investigate before pushing further.
The production readiness checklist
- Circuit breakers on step count and error rate per task
- Active context management with external working memory
- Typed tool schemas with no ambiguous return states
- Structured logging of every tool call input and output
- Distributed tracing across multi-agent workflows
- Regression eval suite run on every deployment
- Defined escalation paths when the agent exceeds its error budget
- Business-metric monitoring, not just infrastructure metrics
Building AI agents is genuinely exciting. The leverage they create — one system making decisions that would have required a team — is real. But that leverage cuts both ways. An unreliable agent at scale causes more damage than a slow human team. Production readiness isn't optional; it's the actual hard part of the job.
