AI agent hallucinations: how to reduce errors

AI agents make mistakes because language models generate plausible text rather than verified facts, and agents use that text to take a series of actions. You cannot eliminate these errors. Measure them on your own tasks, then reduce them with targeted safeguards: supplied sources, structured outputs, limited permissions, human review, and logs.

What counts as an agent hallucination

A hallucination is false output presented with confidence: an invented fact, a nonexistent source, an incorrect number. With a basic chatbot, the error stays in the text. With an agent, it triggers actions: editing a file, sending an email, running a command. Our AI agent or chatbot article explains this distinction, which makes reliability a more serious concern.

Not every agent error involves an invented fact. Five types occur regularly.

Error	What happens	Common cause	Main safeguard
Invented fact	The model states something false	No source in the context	Supply documents and require citations
Incorrect tool call	Wrong parameter or wrong tool	Unclear tool description or unreliable model	Strict schemas and structured outputs
Misread result	The model ignores or distorts a tool’s response	Overly long or noisy result	Summarize, filter, and verify the result
Loop	The agent repeats the same action	No stopping condition	Turn and budget limits
Context drift	The agent forgets an instruction during a task	Overly long history	Reminders, summaries, and controlled memory

Measure before you fix

Without measurements, you are guessing at what needs fixing. The most useful method is simple:

Gather 20 to 50 real tasks for your agent, with the expected answer or action.
Rerun them whenever you change the model, instructions, or tools.
Record the outcome of each task: success, failure, human correction needed, and cost.
Sort failures into the five types in the table above.

Public benchmarks provide a reference point, but they cannot replace your test set. Our guide to reading model rankings explains their limits, and our guide to evaluating an AI agent covers the process in detail. Some benchmarks focus on factual accuracy, such as SimpleQA, published by OpenAI, but they test questions with definite answers, not agent tasks.

A practical example: a support agent answering customers

Consider a fictional agent that answers delivery questions for an online store. This example illustrates the method and uses no real data. The agent has two tools: an order lookup tool and a tool for reading shipping policies.

First incident: a customer asks whether their order will arrive before Friday. The agent cannot find the order, fails to mention this, and gives a “usual” delivery estimate it has guessed. This is an invented fact caused by a missing source. The safeguard is an explicit rule: without a tool result, the agent must say it does not know and hand the request to a human.

Second incident: the lookup tool returns a long record of 40 lines, and the agent quotes the status of a different order. This is a misread result. The safeguard is to return only the fields the agent needs, such as the order number, status, and date, instead of the raw record.

Third incident: the agent repeats the same search five times with the same mistyped number. This is a loop. A turn limit per conversation and a clear failure message stop it.

Each incident needs a fix in a different place: the prompt, the tool, or the configuration. That is why classifying errors helps before you decide to “switch models.”

Build your test set step by step

You do not need sophisticated tools to build a test set. A spreadsheet is enough.

Collect anonymized requests actually received by the agent, or by the person it replaces.
Write the expected result for each one: the correct answer, the action to take, or “refuse and escalate.”
Add challenging cases: missing information, ambiguous requests, conflicting instructions, and malicious text embedded in a document.
Rerun the set after every change and keep dated results for comparison.
Review failures one by one: if the expected result is wrong, fix the test set, not the agent.

Set aside a small share of cases and do not use them to tune the instructions. Use them to check that your fixes also work on cases the agent has not been tuned for.

Safeguards that reduce errors

Provide sources instead of leaving the agent to guess

An agent answering from documents you supply makes up less information than one answering from memory. This is the principle behind RAG, covered in our complete guide. Require traceable citations and reject answers without sources when accuracy matters.

Constrain outputs and tool use

Structured outputs, where code validates a required response format, help prevent incorrectly filled parameters. Describe each tool precisely and limit how many you provide: the more tools an agent has, the more likely it is to choose the wrong one. Tool access protocols such as MCP help standardize these descriptions.

Keep a human involved in risky steps

Human review takes time but prevents the most costly mistakes: payments, deletions, and messages sent externally. Reserve it for irreversible or costly actions, and let the agent handle the rest on its own. Our article on autonomous agents discusses where to draw that line.

Limit permissions

When an agent makes a mistake, the damage depends on what it is allowed to do. Apply least privilege, use a sandbox, and set turn and budget limits to prevent loops.

Keep logs to understand failures

Record steps, tool calls, and results. Without a record, you cannot tell where the agent went wrong. Our guide to monitoring an AI agent covers logs, traces, and alerts.

Choose the right model, which may not be the largest

Larger models often make fewer mistakes, but they cost more, and the gap varies by task. Use the LLM Benchmark to shortlist two or three models, test them on your own test set, and estimate your budget with the price calculator. Our guide to choosing an LLM explains the criteria.

When to accept errors and when to prevent them

Errors have different consequences. You can fix awkward wording in a draft in seconds; you cannot undo a refund sent by mistake. Classify your agent’s actions using two criteria: can you reverse an error, and how much would it cost?

Reversible and low cost (summarizing, categorizing, preparing a draft): the agent can act on its own, with spot checks.
Reversible but visible to others (publishing, sending something to a customer): require human review before sending, at least initially.
Irreversible or costly (paying, deleting, changing permissions): require human review every time, or do not automate.

Adjust these rules as you collect measurements. If your test set shows no errors of a given type for several weeks, you can ease the corresponding checks gradually. Never remove them all at once.

Some errors come from malicious content the agent reads rather than from the model itself. Prompt injection steers an agent into following instructions hidden in a page or email. It can compound hallucinations: an agent that accepts what it reads without checking may act on false information. Our AI agent security article covers the full range of risks, and the Security Lab helps you identify weak points.

Sources to consult

SimpleQA, published by OpenAI: a factual accuracy benchmark using questions with definite answers.
Your model provider’s official documentation on structured outputs and tool calling. Review it whenever you change versions.
Our glossary for the reference definition.

Checklist before you let an agent work

You have a test set of 20 to 50 real tasks and have run it on your chosen model.
Factual answers rely on supplied documents and include citations.
Tools are few in number, precisely described, and called using structured outputs.
Irreversible actions require human review.
The agent’s permissions are limited to what it needs.
Turn and budget limits stop loops.
Steps are logged and reviewed regularly.

What you need to know

No method guarantees zero errors: include a check for every important decision.
An error rate is meaningful only for your tasks: measure it yourself.
Sources and limited permissions do more to reduce errors than choosing a larger model.
Rerun your tests whenever you change the model or instructions.
To understand how the underlying system works, read our explanation of agent memory.

Frequently asked questions

Why do AI agents make up information?

A language model generates the most likely continuation of a text, not a verified answer. When information is missing or the context is ambiguous, it may fill the gaps with something plausible but false. An agent adds the risks of taking action on that output.

Can you eliminate hallucinations completely?

No. You can greatly reduce them within a defined scope by providing sources, limiting actions, and having risky steps reviewed, but no method guarantees zero errors. Always include a check for important decisions.

How do I measure my agent's error rate?

Build a test set of 20 to 50 real tasks with expected results, rerun it whenever you change the model or instructions, and count successes, errors, and human interventions. The figure applies only to your tasks.

Do larger models hallucinate less?

Often, but not always, and they cost more. Compare models on your own tasks: a midsize model with sources and structured outputs can outperform a large model without safeguards.

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