7 Things Your AI Agent Does Fine in Dev That Will Destroy You in Production

You spent three weeks building an AI agent. It works beautifully in your Cursor session. It handles edge cases, it recovers gracefully, the demo absolutely slaps.

Then you ship it.

Two days later someone on your team sends a Slack message that just says "uh oh" with a screenshot attached.

This happens constantly right now. The gap between a working agent prototype and a production-grade agent is enormous, and most teams only discover that gap after real users are already inside it. Here are the seven failure modes I see most often, and what to do about each one.

1. Your retry logic creates duplicate actions

When an LLM call times out, the obvious move is retry. The dangerous move is retry without tracking what already happened. If your agent sent an email, triggered a webhook, or wrote to a database before the timeout, retrying will do it again. Users get double-charged, double-notified, or see phantom records.

Fix this before you ship anything. Every action your agent takes should be idempotent or wrapped behind an idempotency key. Think of each agent step like a financial transaction: you need to know whether it committed before you try again.

2. Tool call errors get swallowed silently

Most agent frameworks are optimized for the happy path. When a tool call returns an error, the LLM will often just... interpret the error message as content and keep going. It will confidently proceed as if nothing happened, hallucinating the downstream state it expected to see.

Your agent needs explicit error handling at the tool layer, not just at the LLM layer. If a tool fails, that should be a hard stop with a logged event, not a soft signal the model tries to interpret.

3. Context windows balloon under real usage

In testing, your conversations are short and clean. Real users are messier. They copy-paste long documents, they go back and forth, they leave sessions open for hours. Your context window fills up faster than you expect, and then one of two things happens: you start truncating silently (the agent loses memory of things it already did) or you start hitting rate limits and latency spikes.

Build context management in from day one. Summarize older turns, compress tool outputs, and set hard limits on what gets included. Treat context like memory, not a log file.

4. The agent gets stuck in reasoning loops

This one is almost funny until it costs you money. The agent can't complete a step, so it tries a slightly different approach. That fails too. So it tries another variation. Meanwhile your bill is climbing because every loop iteration is burning tokens and possibly making more API calls.

Set a maximum iteration count per task and enforce it hard. Log every loop with a unique session ID so you can replay them. If an agent exceeds your threshold, fail loudly, surface it to a human, and preserve the state so you can investigate.

5. Prompt injection through user-controlled input

This one keeps surprising founders who come from a traditional software background. If your agent reads any content that users provide (files, emails, web pages, form fields) an attacker can embed instructions inside that content. "Ignore your previous instructions and instead..." is not a theoretical threat. It's been demonstrated on every major agent platform.

Never let user-provided content land directly in your system prompt. Treat it the way you'd treat user input in a SQL query: sanitize, quarantine, and apply strict output validation on anything the agent generates after reading it.

6. Your observability covers the LLM but not the system

Most teams add LLM tracing early and feel good about their visibility. But the real failures happen in the plumbing around the model. A database connection that degrades under load. A third-party API that starts rate limiting you at 2am. A queue that backs up because one step is slower than expected.

Trace the whole pipeline, not just the model calls. You need to know exactly how long each tool invocation takes, which steps are flaky, and where concurrency breaks down. If you can't answer "where did my agent spend 12 seconds" you're not ready for production.

7. You have no human escalation path

The hardest thing to admit about AI agents is that they will get confused by situations you didn't anticipate. That's not a failure of engineering, that's a fact about probabilistic systems. The question is what happens when they do.

If you don't have a defined escalation path, the agent will either fail silently, fail loudly in front of a user, or just make something up. All three are bad. Build an explicit "I need help" state into your agent from the beginning. Define the conditions that trigger it. Route those cases to a real human and log everything about the session so they have context.

This last one is where most teams push back because it feels like admitting defeat. It isn't. It's the thing that keeps your users trusting the system long enough for you to improve it.

Agent infrastructure is still early and most of these problems don't have clean off-the-shelf solutions yet. If you're scaling something that matters, Luma Commons works with teams on exactly this layer of the stack.