AI Agent Hallucinations: Causes, Types, and How to Prevent Tool Errors

TL;DR

• AI agent hallucinations aren’t just wrong text—they can be wrong actions (bad API calls, wrong parameters, unsafe execution).

• Hallucinations fall into intrinsic (contradicts context), extrinsic (makes up unverifiable facts), and functional/tool (misuses tools/APIs).

• The biggest risk for developers building agentic workflows is functional hallucination: wrong tool selection, malformed arguments, assuming a task is solvable, or bypassing tools.

• Reduce hallucinations with RAG for facts, strict JSON schemas/validation for tool calls, and human-in-the-loop for high-stakes actions, plus logging/observability to debug failures.

When a chatbot hallucinates, it lies to the user. When an autonomous agent hallucinates, it does something wrong. Refunds the wrong customer. Deletes a production database row. Hallucinates a software dependency that introduces a security vulnerability.

That’s the critical shift we’re dealing with as we move from chatbots to autonomous agents.

AI agent hallucination occurs when a Large Language Model (LLM) generates factually incorrect outputs or executes unintended tool actions based on probabilistic patterns rather than grounded data.

Here’s the thing: LLMs are probabilistic, so zero-hallucination models are theoretically impossible. But teams can still achieve enterprise-grade reliability. This guide covers the taxonomy of agentic errors, with a specific focus on functional hallucinations (tool misuse), and provides architectural strategies to prevent them.

To fix agent failures, you need to distinguish between textual confabulation and execution errors.

Confabulation is what most people associate with ChatGPT: the model invents a historical fact or a legal precedent. It’s a retrieval failure. The model fills gaps in its training data with plausible-sounding noise.

Functional Hallucination is specific to agentic workflows. The agent misuses a tool, API, or function. And here’s the tricky part: the agent might understand the user’s intent perfectly but still fail to translate that intent into a valid technical command.

The difference in practice:

• Confabulation: An agent tells a user, “I have applied a 50% discount to your order,” when no such discount exists.

• Functional Hallucination: The agent attempts to call a Shopify API endpoint that doesn’t exist, or tries to pass a string “fifty-percent” into a field that requires an integer 50. Most competitors focus entirely on textual accuracy. But honestly, for developers building agents that interact with CRMs, databases, and external APIs, functional hallucinations pose the greatest operational risk.

Hallucinations fall into three distinct types. You can’t mitigate effectively until you identify which category your agent is struggling with.

Intrinsic hallucinations happen when the model contradicts its own training data or the immediate context in the prompt. You’ll often see this as logical failures. An agent summarizes a document and states a conclusion that directly opposes the text it was given.

Extrinsic hallucinations happen when the model generates information that’s plausible but unverifiable from the source context. These are common in “open book” scenarios. The agent invents a case study or a statistic to support an argument because its parametric knowledge is outdated or incomplete.

This is the most critical category for agent developers. These are errors in planning or executing tool calls. Research identifies four specific subtypes:

1. Tool-Selection Hallucination: The agent picks the wrong tool. Using send_email when the user asked to update_database.

2. Tool-Usage (Parameter) Hallucination: Right tool, wrong arguments. A common example: “hallucinating enums,” where an API expects a status of Active or Inactive, but the agent sends Current.

3. Solvability Hallucination: The agent incorrectly concludes that a task is solvable with the available tools and attempts an execution plan that’s technically impossible. This accounts for over 40% of deep planning errors in complex workflows.

4. Tool-Bypass Error: The agent ignores the provided tools entirely and tries to answer using its internal training data. Usually results in outdated or incorrect information.

A chatbot lie usually costs embarrassment. An agentic error costs real money and real damage.

• Operational Risk: Agents executing flawed workflows can corrupt data integrity. If an agent hallucinates a currency conversion rate while updating a ledger, you’ve got financial discrepancies that may go unnoticed for weeks.

• Supply Chain Risk (Slopsquatting): Coding agents sometimes invent software package names. Attackers now practice “slopsquatting,” registering these hallucinated package names on repositories like PyPI or npm. When a developer (or an autonomous agent) tries to install that non-existent package the LLM suggested, they install malware instead.

• Security and Compliance: “Jailbreaking” an agent can lead to data leakage. If an agent hallucinates that a user has admin privileges based on a vague prompt, it might reveal Personally Identifiable Information (PII).

• Reputational Damage: Public failures erode trust instantly. Remember the Air Canada chatbot that hallucinated a refund policy? A tribunal ruled the airline was liable. Or the DPD chatbot that swore at a customer.

• Cost: Loop-based hallucinations, where an agent repeatedly tries a failed action, waste tokens and compute resources. Your infrastructure bills go up. You get nothing in return.

Agents often lack access to real-time, external knowledge. When you rely solely on the model’s pre-trained weights, you force the model to “guess” facts. That leads to Extrinsic Hallucinations.

• Fix: Retrieval-Augmented Generation (RAG) for Grounding Ground the agent in proprietary or real-time data. Retrieve relevant documentation (a shipping policy, a user’s transaction history) and inject it into the context window. You’re transforming the task from “creative writing” to “reading comprehension.” Add explicit instructions: “answer using ONLY the provided context.” This significantly reduces fabrication.

Vague or incorrect tool definitions force the agent to guess how to use a tool. If an API parameter is described as “the user’s ID” without specifying whether it should be a UUID, an email, or an integer, the model will hallucinate a format. Poor tool definitions are the primary driver of Functional Hallucinations.

• Fix: Structured Outputs and Strict JSON Schemas Enforce strict JSON schemas for all tool inputs. Use the Model Context Protocol (MCP) or OpenAPI specifications to define rigid data models. When the LLM knows exactly what format a tool requires user_id must be an integer, status must be one of “active”, “inactive”), the probability space for errors shrinks. You’re converting a probabilistic problem into a deterministic validation step.

LLMs are next-token predictors, not logic engines. In long conversations, the “lost in the middle” phenomenon causes agents to forget instructions or constraints buried in a large context window. The result is logical drift.

• Fix: Chain-of-Thought Prompting (and When to Avoid It) Prompting the agent to “think step-by-step” improves reasoning. But it’s a double-edged sword. Recent studies show that while CoT improves planning, it can sometimes amplify tool hallucination rates (the “Reasoning Trap“) if the model reasons itself into false confidence. Use CoT alongside strict schema enforcement. Not as a replacement.

For high-stakes tasks, software-defined prevention isn’t enough.

• Pattern: Human-in-the-Loop (HITL) Guardrails Implement threshold-based escalation. If an agent’s internal confidence score drops below a certain percentage, or if it detects a high-stakes action (like transfer_funds), route the request to a human for approval. This ensures that “Solvability Hallucinations” don’t result in catastrophic actions.

Strategy Best for Implementation Difficulty Primary Benefit Prompt Engineering (CoT) Logic/Reasoning errors Low Improves reasoning transparency; helps with complex planning. RAG (Grounding) Factual/Knowledge errors Medium Connects agent to real-time truth; prevents stale data errors. Strict Tool Schemas Functional/Action errors Medium Prevents invalid API calls and parameter hallucinations. Human-in-the-Loop High-stakes actions High (Operational cost) Ultimate safety net for critical failures.

Eliminating hallucinations often costs autonomy and speed. Creativity and accuracy trade off against each other.

• The “Temperature” Dial: Lowering the temperature (making the model more deterministic) reduces hallucinations but makes the agent rigid. Less capable of handling nuance or edge cases.

• Latency Cost: Heavy guardrails add latency. Self-correction loops where an agent reviews its own output, or verification steps using a second LLM, take time.

• Decision Framework: Your engineering team needs to decide where to optimize. For a banking agent, zero-hallucination is non-negotiable. That justifies higher costs and latency. For a creative brainstorming agent, higher hallucination rates may be acceptable in exchange for speed and novelty.

Hallucination prevention works best at the infrastructure layer, not just through prompt engineering.

• Schema Optimization: Don’t pass raw, bloated API docs to the LLM. Create a middleware layer that provides cleaned, minified schemas to reduce cognitive load.

• Token Abstraction: Use a “Brokered Credentials” pattern where the agent never sees the API key. Instead, it calls a proxy that injects the auth headers, preventing the model from “guessing” or leaking sensitive tokens.

• Execution Traceability: Implement detailed logging of the “thought-to-action” pipeline. You need to see the exact prompt, the generated JSON, and the resulting API response to debug hallucination patterns. By treating agent-tool integrations as managed infrastructure rather than ad-hoc scripts, you can build systems that are significantly more reliable.

An AI agent hallucination occurs when an LLM produces incorrect information or takes an unintended action. In agents, the most damaging form is executing the wrong tool call (wrong endpoint or parameters), not just generating wrong text.

Confabulation is fabricated or incorrect text. Functional hallucination occurs when the agent misuses tools/APIs, whether that’s choosing the wrong tool, sending invalid arguments, or executing an unsafe plan.

Intrinsic hallucinations contradict the prompt/context. Extrinsic hallucinations invent unverifiable facts beyond the context. Functional/tool hallucinations are execution errors in tool calling and workflow steps.

Use strict tool schemas (JSON Schema/Pydantic), validate inputs before execution, and fail closed on invalid arguments. Add observability to inspect tool calls and implement human approval for high-risk actions.

Use RAG to reduce factual/extrinsic hallucinations by grounding answers in trusted documents or real-time data. Use strict schemas to reduce functional hallucinations by constraining tool inputs and preventing invalid API calls.

Solvability hallucination occurs when an agent assumes it can complete a task with the available tools and proceeds anyway. It leads to broken plans, repeated failures, and sometimes unsafe fallback behavior.

Slopsquatting occurs when attackers register package names that LLMs commonly “invent.” If a developer or coding agent installs the hallucinated package, it can introduce malware into the supply chain.

Lower temperature can reduce randomness and lower hallucination rates, but it won’t eliminate them. You typically need grounding (RAG), validation (schemas), and safety controls (HITL) alongside decoding settings.