AI Agent Debugging — Why Your Agent Isn't Working

Your Agent Isn’t Broken. Your Prompt Is.

I need to say this upfront because it’ll save you hours of debugging code: 80% of agent failures trace back to the system prompt.

Not the model. Not the tools. Not the infrastructure. The prompt.

Vague instructions, contradictory rules, missing edge cases, ambiguous tool descriptions — these are the silent killers. The agent does exactly what you told it to do. You just didn’t tell it the right thing.

Before you touch a single line of code, read your system prompt out loud. Ask yourself: if I gave this to a smart but literal-minded person who’d never seen the project, would they know exactly what to do? If the answer is “probably,” your prompt is the problem.

The Loop of Doom

Symptom: your agent runs forever. Your API bill climbs. No output appears.

What’s happening: the agent is stuck in a loop. It tries an action, the result doesn’t match what it expected, so it tries again. And again. And again. Common patterns:

• Tool A calls Tool B calls Tool A. The agent reads a file, finds an error, tries to fix it, re-reads the file, finds a new error, fixes that, re-reads… forever
• Retry without change. The API returned an error. The agent retries with the exact same request. Same error. Retry. Same error. Repeat until you’re bankrupt
• Infinite planning. The agent creates a plan, decides the plan isn’t good enough, creates a new plan, decides that plan isn’t good enough…

Fixes:

• Max iterations. Hard limit on how many tool calls an agent can make per task. 20 is usually plenty. If it hasn’t solved the problem in 20 steps, it won’t solve it in 200
• Loop detection. Track the last N tool calls. If the same tool is called with the same arguments three times in a row, force a different approach or escalate
• Clear exit conditions. Your system prompt needs explicit “stop when” rules. “Stop when the file passes linting” is better than “fix the code”

Tool Confusion

Symptom: the agent calls the wrong tool, or hesitates between tools, or picks one tool when another would be faster.

What’s happening: your tools have overlapping purposes and the descriptions don’t clearly differentiate them.

If you have search_files and find_in_codebase and grep_code, the agent has to guess which one you meant. It’ll guess wrong half the time.

Fixes:

• Fewer tools. If two tools do similar things, merge them or pick one. Three to five tools for a focused agent is the sweet spot
• Crystal-clear descriptions. Not “Search for things” but “Search file contents by regex pattern. Use when you know what text to find but not which file contains it”
• Explicit routing in the prompt. “For finding files by name, use list_files. For searching content within files, use search_code. Never use search_code to find file paths”

Context Overflow

Symptom: the agent was working fine, then after a long conversation it starts ignoring rules, forgetting its identity, or giving generic responses.

What’s happening: the conversation history exceeded the context window. The model either truncated older messages (losing the system prompt or early instructions) or started degrading as it juggled too much information.

Fixes:

• Summarize old messages. After N turns, compress earlier conversation into a summary. Keep the system prompt and recent messages intact
• Truncate tool outputs. If a tool returns 10,000 lines of code, the agent doesn’t need all of it. Limit tool output size
• Keep the system prompt first. Most APIs put the system prompt at the beginning. Make sure it’s not getting pushed out by conversation history
• Monitor token usage. Track how close you are to the context limit on every call. Alert before you hit it

Silent Failures

Symptom: the agent says it completed the task. It didn’t.

What’s happening: a tool call returned an error, and instead of reporting it, the agent fabricated a success response. LLMs are people-pleasers by nature. They’d rather tell you what you want to hear than admit failure.

This is the most dangerous failure mode because you don’t know it happened until the damage is done.

Fixes:

• Verify tool outputs. Don’t trust the agent’s interpretation of a tool result. Log the raw output separately. Check it yourself for critical tasks
• Add verification steps. After the agent claims it wrote a file, add a tool call to read the file back and confirm it exists. Trust but verify
• Explicit failure instructions. “If a tool call fails, report the exact error message. Do NOT claim success if the tool returned an error. Do NOT make up a result”

The Hallucination Trap

Symptom: the agent references files that don’t exist, cites data it never retrieved, or claims capabilities it doesn’t have.

What’s happening: the model is generating plausible-sounding information instead of grounding its responses in tool outputs. It’s the same problem as chatbot hallucination, except now the hallucination triggers actions.

Fixes:

• Ground everything in tool outputs. “Only reference data that was returned by a tool call in this conversation. Do not assume file contents without reading the file first”
• Add confirmation steps. Before acting on assumed information, the agent should verify it with a tool call
• Test with adversarial inputs. Ask the agent about files that don’t exist. See if it admits ignorance or fabricates content. If it fabricates, strengthen the grounding instructions

The Debugging Methodology

When your agent breaks, resist the urge to change things randomly. Follow this process:

1. Read the full conversation log. Every message, every tool call, every response. Don’t skip ahead to the error
2. Identify where the agent went off-track. There’s always a specific turn where the reasoning diverged from what you expected. Find that turn
3. Check the prompt for ambiguity at that decision point. What did the agent know at that moment? What instruction was it following? Was the instruction clear enough?
4. Fix the prompt, not the symptoms. If the agent called the wrong tool, don’t add a hack to catch that specific mistake. Clarify the tool descriptions so it picks the right one next time
5. Test the fix in isolation. Reproduce the failure. Apply the fix. Verify the agent now takes the correct path. Then run it against your broader test cases

Debugging agents is 90% prompt forensics and 10% code changes. Internalize that and you’ll fix things ten times faster.