Building AI Agents That Don't Fall Apart in Production

You shipped the demo. It worked beautifully. Your agent answered questions, called APIs, chained tools together like magic. Then real users showed up and the whole thing started hallucinating, looping, and timing out in ways you never saw coming.

This is the gap nobody talks about enough. Building a proof-of-concept AI agent is genuinely easy now. Building one that runs reliably in production, at scale, with real users doing unexpected things — that's a different problem entirely. Here's what actually separates the agents that last from the ones that get quietly killed off.

1. Treat Your Prompts Like Production Code

Most teams treat prompts as an afterthought. They live in a string literal somewhere, nobody reviews them, and they change constantly without any record of what broke when.

Version control your prompts the same way you version control your code. Keep them in dedicated files, review changes in PRs, and log which prompt version was active for every agent session. When something goes wrong — and it will — you need to be able to trace it back to a specific prompt change.

2. Build a Tool Call Budget Before You Ship

Agents that can loop infinitely are agents that will loop infinitely. One edge case, one unexpected API response, and your agent is stuck in a cycle burning tokens and money until something external kills it.

Set a hard maximum on tool calls per session before you write a single line of agent logic. Ten calls is a reasonable starting point for most tasks. If your agent needs more than that to complete a job, it's a signal the task is too complex and needs to be broken into smaller, more predictable workflows.

3. Never Trust Tool Output Blindly

Your agent calls an API and gets back a response. That response might be malformed, truncated, in an unexpected schema, or contain injected instructions if you're dealing with user-supplied content. Passing that output directly back into the model context is a real risk.

Validate every tool response before it touches your model. Define the exact shape you expect, reject anything that doesn't match, and return a structured error message the agent can reason about instead. This single habit eliminates a huge class of production failures.

4. Separate Planning From Execution

The most brittle agents try to think and act at the same time. They generate a plan and immediately start executing it, with no checkpoint in between. One bad assumption in step one cascades into a completely broken result by step five.

Structure your agent so it produces a plan first, then you validate that plan before execution starts. For high-stakes actions, surface the plan to the user for confirmation. This adds one round-trip but saves you from a category of failures that are extremely hard to debug after the fact.

5. Log Everything Your Agent Thinks, Not Just What It Does

Most teams log the inputs and outputs of their agent. That tells you what happened but not why. When something goes wrong, you're left guessing at the reasoning chain that led to the bad outcome.

Log the full model response at every step, including the chain-of-thought if you're using it. Yes, this gets verbose. Yes, you'll thank yourself later. A structured log of agent reasoning is the closest thing you have to a debugger for a non-deterministic system.

6. Design Fallback Behaviors Before You Need Them

What does your agent do when a tool is down? When the model returns something unparseable? When it hits a rate limit mid-task? Most teams find out the answer to these questions the hard way, in production, with a user watching.

For every tool your agent depends on, write an explicit fallback behavior before you ship. It can be as simple as returning a graceful error message and stopping. The point is that the behavior is intentional and tested, not an accidental unhandled exception.

7. Monitor Agent Sessions With the Same Rigor as API Endpoints

Your backend probably has dashboards for latency, error rates, and throughput. Your AI agent probably has none of that. This creates a blind spot that will bite you.

Track completion rate, average tool calls per session, sessions that hit your max budget, and session duration. These four metrics alone will surface most production problems within hours of a bad deploy. Treat agent observability as a first-class feature, not a nice-to-have.

8. Rate Limit at the User Level, Not Just the API Level

Your model provider has rate limits. But so should your users. An agent that lets a single user spin up unlimited parallel sessions is an agent that will occasionally get hammered by one bad actor or one runaway script and degrade service for everyone else.

Implement per-user concurrency limits and daily usage quotas from day one. This is especially important if you're giving users the ability to trigger agents programmatically. The cost and reliability implications of ignoring this are both painful.

9. Test With Adversarial Inputs Before Launch

Your QA process probably covers the happy path. It probably does not cover a user who pastes in a 50,000 word document, asks contradictory questions in the same session, or deliberately tries to make your agent take actions outside its intended scope.

Add an adversarial testing phase to your agent release process. Have someone on your team actively try to break the agent, not just use it correctly. The failures you find internally are much cheaper to fix than the ones your users discover.

10. Plan for the Model to Change Under You

The model your agent depends on today will be updated, deprecated, or replaced within months. Behavior will shift. Things that worked will stop working. If your agent is tightly coupled to one specific model version, you're accumulating technical debt with every passing week.

Abstract your model calls behind an interface from the start. Make swapping models a configuration change, not a refactor. Run regression tests against your agent's core behaviors every time you update a model dependency. This is the kind of boring infrastructure work that separates teams who ship durable products from teams who ship demos.

If you're building production AI agents and want a second set of eyes on your architecture, the team at Luma Commons has been through these failure modes and can help you skip the expensive lessons.