AI agents fail in a few predictable ways. Once you can name them, you can catch them early and recover calmly.
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This is Part 6 of Your First AI Agent. New here? Start at Part 1. Up next: Multi-Step Workflows — Chaining Tasks Without Babysitting.
Mistakes Are Normal, Not Scary
Let's start with the honest truth. Your agent will make mistakes.
That is not a reason to stop using one. A new human assistant makes mistakes too. You do not fire them. You learn how they slip, and you build a few habits.
This article is about those habits. We will look at the common ways agents go wrong. Then we will turn each one into a sign you can spot.
By the end, a bad run will not rattle you. You will see it coming, catch it early, and fix it.
Think of yourself as the calm pilot. The agent does the flying. You watch the instruments and you keep your hand near the controls.
The Four Ways Agents Go Wrong
Most agent failures fall into four buckets. Learn these names and you are halfway to spotting them.
- Wrong assumptions. The agent guesses a missing detail and runs with it.
- Loops. The agent repeats the same action and never moves forward.
- Made-up facts. The agent invents information that sounds real. This has a name: a hallucination.
- Overconfidence. The agent reports success in a sure voice, even when the work is wrong.
We will take each one slowly. None of them are mysterious once you know the pattern.
Wrong Assumptions: The Quiet Guess
This is the most common failure, and the easiest to miss.
When your instructions leave a gap, the agent does not stop and ask. It fills the gap with a guess and keeps going.
Say you ask it to "clean up the spreadsheet." Clean up how? It picks a meaning. Maybe it deletes blank rows you wanted to keep. It was not being careless. It simply assumed.
The agent treats a vague word as a clear command. A hint becomes an order. A suggestion becomes a rule.
How to Spot a Wrong Assumption
Before the agent acts, ask it to show its plan. Most agent tools let you see the plan first, or you can request it.
Read the plan and look for choices you did not make. If a step says "I will remove all rows with missing values," and you never said that, you just caught an assumption before it caused harm.
Tip
Add one line to your instructions: "If anything is unclear, ask me before you act." This single sentence turns silent guesses into questions. It is the cheapest fix in this whole guide.
We covered how to write clear, gap-free instructions in Part 4. Tight instructions are your best defense against wrong assumptions. The clearer your brief, the fewer gaps the agent has to guess about.
Loops: When the Agent Spins Its Wheels
A loop is when the agent repeats the same kind of action and never makes progress.
It searches the same term over and over. Or it tries a step, hits the same error, and tries the exact same step again. The run keeps going, but nothing new happens.
Loops waste time and money. Each repeated step can cost a little, and those add up fast. A looping agent is like a car with the wheels spinning in mud. Lots of motion, no movement.
How to Spot a Loop
The signs are clear once you know them.
- Repeated actions. The same search, the same file read, the same tool call again and again.
- A climbing step count. The number of steps grows, but the result does not change.
- The same error, on repeat. It hit a wall, and it keeps walking into the same wall.
When you see this, stop the run. You do not need to wait it out. Almost every agent tool has a stop or pause button. Use it.
Warning
A looping agent will not always stop on its own. It can run for a long time, burning time and money, while looking busy. Set a step limit or a time limit when your tool allows it. Then a runaway loop hits a ceiling instead of running forever.
After you stop a loop, the fix is usually in your instructions. The agent got stuck because it had no way out of a dead end. Give it one. Tell it: "If a step fails twice, stop and tell me what went wrong instead of retrying."
Find the contact email for this company.
Find the contact email for this company. If you cannot find it after two tries, stop and tell me. Do not keep searching the same way.
The second version gives the agent permission to give up gracefully. That one rule prevents most loops.
Made-Up Facts: The Confident Invention
You may remember hallucinations from earlier in the series, or from using AI as a chatbot. Agents do this too, and it can be sneakier.
Here is why. An agent does not look up facts in a database. It predicts likely words. When it lacks a real answer, it produces fluent words that sound right anyway.
So an agent might invent a customer name, a fake link, a wrong number, or a step it never actually did. The made-up part looks exactly like the real part. Same calm tone, same tidy format.
With an agent, this is riskier than with a chatbot. The agent acts on its own output. If it invents a fact in step two, it may use that fake fact in step five. One small invention can spread through a whole task.
How to Spot Made-Up Facts
You catch hallucinations by checking the real work, not the summary.
- Open the actual output. If the agent says it sent an email, look at the sent folder. If it says it found a price, find that price yourself.
- Be careful with specifics. Exact numbers, names, dates, and links deserve a second look. These are where inventions hide.
- Ask for sources. Tell the agent to show where each fact came from. Then confirm those sources are real.
Tip
Add this to your brief: "For every fact you report, tell me where it came from. If you are not certain, say so instead of guessing." Giving the agent permission to say "I don't know" is one of the most useful lines you can write.
A free prompt scorer can help you tighten a brief before you run it, so the agent has fewer gaps to fill with guesses in the first place.
Overconfidence: When "Done" Doesn't Mean Done
This one ties the others together.
An agent reports its work in a sure, smooth voice. "All set! I cleaned the data and sent the report." It sounds finished. It sounds correct.
But the agent has no real sense of whether it succeeded. It writes "done" the same confident way whether the job is perfect or broken. Confidence is its default style, not proof of results.
So the trap is simple. You read a happy summary, you relax, and you never check the actual work. Meanwhile the report went to the wrong person, or the data is half-deleted.
Warning
Never treat an agent's summary as proof. A tidy "task complete" tells you the agent stopped, not that it succeeded. The summary is a claim. The output is the evidence. Check the evidence.
The habit here is short. When the agent says it is done, you open the result and confirm with your own eyes. For anything that matters, the agent reports and you verify.
| What the agent says | What you actually check |
|---|---|
| "I sent the email." | Open the sent folder and read it. |
| "I found the cheapest option." | Look at the price and the source. |
| "I updated the file." | Open the file and scan the changes. |
| "Everything looks good." | Decide that for yourself. |
Your Recovery Routine
So a run went wrong. What now? You do not need to panic or start over from scratch. Follow a calm routine.
Stop the run. If the agent is still going, pause or stop it. Do not let it build more steps on a bad one.
Find where it went wrong. Read the steps. Look for the moment a wrong assumption, loop, or made-up fact appeared.
Ask why. Usually a gap in your instructions, a vague word, or a missing check let it happen. Name the real cause.
Fix the brief. Add the missing detail. Add a rule like "ask first if unclear" or "stop after two failures."
Run small and verify. Try a smaller version first. Confirm it is right before you hand over the full task again.
Keep the bad output until the new run proves itself. That way you can compare, and you have a safety net if the second try also slips.
One more thing. When an agent makes a mistake, the answer is rarely to scold it. Scolding does not teach it. Better instructions do. Treat each mistake as a note for your brief, not a personal failing of the agent.
5 steps
Match Your Watching to the Stakes
You do not need to hover over every single run. That would be exhausting, and it would defeat the point of delegating.
The trick is to match your attention to the stakes.
For low-stakes tasks, relax. If the agent is drafting ideas, summarizing an article, or sorting files you can easily undo, a quick glance at the result is plenty. A small mistake here costs you nothing.
For high-stakes tasks, watch closely. Anything that sends messages, spends money, deletes data, or touches other people deserves your full attention. Read the plan, approve risky steps, and verify the output.
We covered approvals and permissions in detail in Part 5. Those guardrails and this watching habit work together. Guardrails stop the worst mistakes. Watching catches the rest.
Tip
Before a run, ask yourself one question: "If this goes wrong, how bad is it?" Cheap to undo means a light touch. Hard to undo means stay close. Let that answer set your level of attention.
You Can Handle a Bad Run Now
Let's tie it together.
Agents fail in four predictable ways. They guess at missing details, they get stuck in loops, they invent facts, and they sound confident even when they are wrong.
None of those should scare you anymore, because now you can spot each one. You read the plan to catch assumptions. You watch for repetition to catch loops. You check the real output to catch made-up facts and overconfidence.
And when something does slip, you have a routine. Stop, find the cause, fix the brief, and run small again. Calm and steady, every time.
The people who get burned by agents are the ones who trust a happy summary and never look. You will not, because you know better now. You watch the instruments, and you keep your hand near the controls.
That is the whole job. Not fear. Just a few good habits.
Keep going
Next → Part 7: Multi-Step Workflows — Chaining Tasks Without Babysitting
Or see the full Your First AI Agent series.