7 Mistakes That Doom AI Agents in Production
AI Systems

7 Mistakes That Doom AI Agents in Production

The most common failure modes for autonomous AI agents and how to prevent them before they cost you.

7 Mistakes That Doom AI Agents in Production

I have shipped three AI agents to production and watched each of them fail in different ways before they became stable. Here are the seven mistakes that caused the most incidents.

1. No Hard Token Budget Per Invocation

Agents that can call themselves recursively or chain many LLM calls will occasionally enter runaway loops. Without a hard budget, a single malformed user input can trigger hundreds of LLM calls and cost $50 in minutes. Always set a per-run token budget and alert on budget exhaustion.

2. Not Handling Tool Call Failures Gracefully

LLM tool calls fail. The API goes down, the schema mismatches, the external service returns a 500. Return typed error objects the LLM can use in its next reasoning step instead of raising unhandled exceptions.

3. Trusting LLM Output for Security Decisions

Never use raw LLM output to make access control, authentication, or financial decisions without a validation layer. Prompt injection can make the model claim permissions it should not have.

4. Synchronous Blocking Architecture

Agents with multi-step reasoning take 5-30 seconds to complete. Blocking a web server thread for that duration will hurt your throughput. Use async task queues for agent runs.

5. No Structured Logging Per Agent Step

When an agent produces wrong output, you need to reconstruct exactly what happened: which tools were called, what the LLM said at each step, what the token counts were. Without structured per-step logs, debugging is guesswork.

6. Ignoring Context Window Management

As conversation history grows, you will eventually hit the context window limit. Handle this explicitly by trimming history — always keep the system prompt and last N turns, summarize or drop middle turns.

7. Deploying Without an Eval Suite

Before each deploy, run your agent against a fixed set of 50-100 test cases and gate on pass rate. Without this, you are flying blind after every update.

These seven issues account for roughly 80% of production incidents I have seen or caused with AI agents.