AI Agent Hallucination: Causes, Risks, and Fixes

There are real, documented stories of drivers following GPS directions so faithfully they ended up in a lake. The device routed them down a boat launch. They followed it anyway.

It's the kind of story that makes everyone laugh and nobody learns from.

Brianne Zavala opens her IBM Technology explainer on AI agent hallucination with exactly that image, and it's a better frame than most. Because the question she's actually asking isn't "is AI broken?" It's: what happens when a system that's been trained to sound confident encounters a situation it genuinely doesn't know? And more pressingly—what happens when that system doesn't just answer you, but starts doing things on your behalf?

That's the shift from chatbots to agents, and it's where hallucination stops being a curiosity and becomes an operational risk.

The thing about hallucination is that it's not a glitch

Here's what trips people up: hallucination isn't the model malfunctioning. It's the model doing exactly what it was built to do.

Large language models don't look things up. They predict. When you ask one a question, it's generating text that matches the statistical shape of a correct answer—which is impressive and, honestly, kind of wild when you think about it. But "sounds like the right answer" and "is the right answer" are not the same thing, and the model has no reliable way to tell the difference.

Zavala lays out three mechanics driving this: models generate plausible answers, not verified ones; they're trained to sound confident (hesitation gets penalized during training, fluency gets rewarded); and when data is missing, they improvise rather than stop and ask for clarification.

That last one is worth sitting with. The improvisation sounds identical to a real answer. There's no asterisk. No hedge. The model fills the gap with the same tone it uses when it actually knows something.

Her procurement example makes this concrete: an AI agent helping validate vendor contract dates pulls from available data, but if the date simply isn't there? It manufactures one. Confidently. Your team then works from a deadline that never existed. "That's not a bug in the traditional sense," Zavala says. "It's the model doing exactly what it was built to do—to complete the pattern and fill that gap. But the problem is pattern completion and truth aren't always the same thing."

Agents: better at hallucinating less, but worse when they do

Here's where the agentic evolution complicates things, and Zavala is careful to hold the tension rather than collapse it into either optimism or alarm.

The case for agents being better on hallucination: when an agent has access to real tools—search, APIs, data connectors, retrieval systems like RAG (Retrieval-Augmented Generation, which means the model pulls from a connected knowledge base rather than relying on what it learned during training)—it can actually verify information before responding. Instead of guessing, it checks. That's a genuine and meaningful improvement over a bare LLM generating answers from memory.

The case for agents being worse when things go sideways: an agent isn't just generating text. It's taking actions. Creating tickets. Updating records. Scheduling meetings. Modifying data that downstream systems depend on. A hallucination in a chatbot produces a wrong answer. A hallucination in an agent can produce a wrong action—executed autonomously, at scale, with complete confidence, across a multi-step reasoning chain where each wrong step compounds the next.

Zavala also flags something worth watching: some of the more advanced "thinking" models—the ones that do extended internal reasoning before responding—actually show higher hallucination rates in certain evaluations. More capability doesn't automatically mean more accuracy. Sometimes it just means more elaborate wrongness.

Four levers that actually move the needle

Zavala runs through four design choices that reduce hallucination risk in practice. None of them are magic; all of them require intentional implementation.

Ground the agent in real data. An AI agent with no access to your actual systems is like hiring a consultant and refusing to show them anything. They'll still give you an answer—they just won't have any basis for it. Connecting agents to live sources of truth (SharePoint, CRMs, contract repositories, APIs) gives the system something to check against rather than something to predict around. The old GPS with the five-year-old map isn't wrong about everything—it just doesn't know about the road that closed last month.

Replace text prediction with tool-based reasoning. If you asked a colleague to calculate your quarterly burn rate, you'd want them to open a spreadsheet, not recite figures from memory. An agent that can call a calculator, query live data via API, or retrieve a specific document before answering is fundamentally different from one that's predicting what the answer should look like. "That's the difference between the agent that predicts and one that verifies," Zavala puts it. "And that's the shift that actually moves this needle."

Define the scope explicitly. Hallucination grows at the edges—when the agent operates outside its domain and doesn't know to say so. Zavala's analogy here is sharp: think of someone who's a genuine expert on their home city, but you ask them about a place they visited once a decade ago. They don't say "that's outside my knowledge." They answer with the same confidence they bring to everything. That's an agent without scope boundaries—"highly capable inside their lane, but genuinely dangerous when they're operating outside of it." The fix is being explicit: what can this agent do, what can't it do, where does its data come from, and which workflows require human sign-off before action.

Keep a human in the loop for high-stakes decisions. Not as a failure mode—as a design feature. Zavala's framing here is worth quoting directly: "The agent proposes, a human reviews, and actually does that final approval. If it's not a failure of the technology, it's good system design." The cruise control analogy lands: it handles steady highway miles fine, but you still want a driver for the on-ramp. Human-in-the-loop adds judgment, contextual understanding, and accountability that no model currently replicates—and it catches hallucinations before they become actions.

What this framing doesn't resolve

Zavala's perspective is practical and well-constructed, but it comes from IBM—a company with enterprise AI products to sell. That doesn't make the analysis wrong, but it's worth noting that the frame stays almost entirely at the implementation layer. The solutions presented assume well-resourced teams who can actually implement data grounding, define agent scope, and build human review into their workflows.

For smaller organizations, or for cases where AI agents are already deployed without these guardrails in place, the practical picture looks different. The research literature on hallucination reduction is more complicated than four clean levers—mitigation strategies that work in one domain often degrade in another, and the benchmarks used to measure hallucination rates vary enough that "higher" and "lower" can mean different things in different evaluations.

There's also a deeper question that design choices can't fully address: models trained to prioritize fluency over epistemic humility will continue to hallucinate until the underlying training incentives change. Some newer models are improving at expressing uncertainty—Zavala acknowledges this—but "it's still the exception, not the rule."

The four levers are real and worth implementing. They're just not a solved problem wearing a solved-problem costume.

The GPS-into-the-lake story works because the failure mode is obvious in retrospect. You can see the boat launch. You can see the water. The driver's mistake is legible.

Agent hallucination is harder to see—especially when the output is a data field update, a scheduled meeting, or a procurement deadline that exists nowhere except in the model's prediction of what the data should have said. The system that drove you into the lake won't tell you it's happening.

Which is maybe the most honest thing to say here: the design choices Zavala describes reduce the risk. They don't eliminate the need to keep looking at where the road is going.

Yuki Okonkwo is Buzzrag's AI & Machine Learning correspondent. She covers the systems shaping our infrastructure and the people building them.