Why Autonomous Agents Fail: 6 Engineering Mistakes to Avoid

I have reviewed the logs of hundreds of failed agent runs. The failure patterns are depressingly consistent. A developer builds a cool demo, it works 50% of the time, and then they deploy it to production where it promptly sets money on fire or crashes.

Autonomous Agents are non-deterministic state machines. This means they can enter invalid states that you didn't explicitly program. If you don't engineer defensive guardrails, your agent *will* go off the rails.

Here are the top 6 engineering mistakes that kill agentic projects.

Mistake #1: The Infinite Loop (The Money Burner)

Agent decides to 'Search Google'. It finds nothing. It decides to 'Search Google' again to be sure. It interacts with the tool 500 times in 1 minute. You wake up to a $2,000 OpenAI bill.

**The Fix:** Hard limits. Every agent execution graph must have a `recursion_limit` (e.g., max 10 steps). If it hasn't solved the problem by step 10, kill the process and return error.

Mistake #2: Context Pollution

You let the agent scrape a Wikipedia page and dump the entire 50,000 tokens of raw HTML into the chat history. Now the context window is full. The model forgets its original instruction because it's buried under `<div>` tags.

**The Fix:** Sanitization. Never feed raw data. Use an LLM or script to summarize/extract *only* the relevant data before appending it to the conversation history. Keep the context lean.

Mistake #3: Ambiguous Tool Schemas

You define a tool `search_database(query: string)`. The Agent asks: "recent users". The database errors out because it expects SQL, not natural language.

**The Fix:** Prompt engineering *inside* the tool definition. `search_database(sql_query: string) - Executes a valid SQL query against the Users table.` Be explicit about the input format.

Mistake #4: Ignoring JSON Errors

LLMs are bad at generating perfect JSON. They miss a comma or a bracket. Your parser crashes. The whole workflow fails.

**The Fix:** Use 'Auto-Correction'. If the JSON parse fails, feed the error message *back* to the LLM: "You generated invalid JSON here. Fix it." The LLM will correct itself in the next turn.

Mistake #5: Single-Agent Monoliths

Trying to make one prompt do Research, Writing, Coding, and Reviewing. The instructions get too long. The model gets confused.

**The Fix:** Decomposition. Split it into 4 different Agents with 4 simple system prompts. Orchestrate them.

Field Note: I audited a code-writing agent that kept getting stuck. It turns out the agent was writing code, getting a syntax error, and then retrying the *exact same code* because it didn't look at the error message properly. We simply added the `stderr` output to the next prompt, and the agent started fixing its own bugs.

The difference between a toy agent and a pro agent is robustness. Anticipate failure. Assume the LLM will hallucinate, the tool will timeout, and the JSON will be malformed. Design your system to survive these failures, and your agent will thrive.