Why AI Agents Fail: 8 Failure Patterns and How to Fix Them

Eight common AI Agent failure patterns, drawn from the MAST research and Anthropic's lessons, plus a practical debugging process.

"The quotation has been sent to the customer." A contractor's AI Agent reported this with confidence. Three days later the customer called asking where the quote was. Only after opening the logs did the team discover that the agent had drafted the email but never pressed send. If this has happened to you, you are not alone, and the good news is that most agent failures follow "patterns" you can spot and fix.

What is a failure mode

A failure mode is a repeatable pattern of failure, not a random mistake. The paper Why Do Multi-Agent LLM Systems Fail? (Cemri et al., 2025) analysed more than 1,600 traces from 7 popular frameworks and identified 14 failure modes in 3 groups: system design issues, inter-agent misalignment, and task verification. This article picks 8 common patterns and combines them with lessons from Anthropic's engineering team.

8 failure patterns and how to fix them

1. Repeating the same step and not knowing when to stop

The paper calls these step repetition and unaware of stopping conditions. Fix: define "done" clearly, set a maximum number of rounds (for example, n8n's AI Agent node has Max Iterations with a default of 10), and flag repeated calls to the same tool with the same values.

2. Saying it is done when it is not

This matches premature termination, and it is the quotation case above. Fix: check the outcome in the real system, such as the sent folder, instead of trusting the agent's message.

3. Incomplete or incorrect checking

No or incomplete verification, and incorrect verification. Fix: use code checks for anything measurable, such as whether totals add up, and have a separate agent check the work rather than the one that produced it.

4. Calling the wrong tool or passing wrong values

Anthropic describes an agent that made mistakes with relative file paths after moving out of the root directory, until the team required absolute paths. Fix: design tools that are hard to misuse and write errors that explain the fix. See What is Tool Calling.

5. Forgetting what was discussed

Loss of conversation history, along with context rot, where accuracy drops as context grows. Fix: compaction, and have the agent keep key notes outside its context. See AI Agent Memory.

6. Ignoring the spec and not asking when unclear

Disobey task specification and fail to ask for clarification. Fix: write instructions as numbered points, state what is not allowed, and say explicitly that it should ask when information is missing.

7. Duplicated work and information dropped between agents

Information withholding and ignored other agent's input. Anthropic found that vague delegation made subagents duplicate work, and early versions spawned 50 subagents for simple queries. Fix: a brief with an objective, an output format and task boundaries.

8. Being tricked by external content

Web pages or emails with hidden instructions (prompt injection) make the agent do things nobody asked for. Fix: limit permissions and require human approval for risky actions. Read more in Guardrails and Human-in-the-loop.

A debugging process that works

1. Capture full traces: every prompt, tool call, argument and result
2. Reproduce it: run the same input several times, because AI output varies between runs
3. Classify it: compare with the 8 patterns above to know whether to fix the prompt, the tool or the system structure
4. Fix one thing at a time: change several things at once and you will not know what worked
5. Add the case to your test set: so the same problem does not return. See Evals

Watch out for

• Do not blame the model first: many problems come from tools or vague instructions. Switching models may just move the problem
• Do not fix by endlessly adding to the prompt: an ever-longer prompt can confuse the agent itself
• Mind the data in your logs: traces may contain customer data, so restrict who can access them

An agent that has never failed is an agent nobody has really used. What matters is that you can see where it fails.

What to do next

1. Turn on trace logging for every agent you run
2. Read a random sample of 10 traces a week and note which patterns you see
3. Fix the most frequent pattern first
4. Turn every failed case into a test task

Debugging AI systems is a skill that applies across engineering, QA and operations roles. If you want to explain this experience clearly to an interviewer, practise with Plants.work's Interview Lab.