How to Build AI Agents That Don’t Hallucinate When Decisions Matter
Why architecture determines reliability, not bigger models.
Most tutorials about AI agents assume a world that doesn’t really exist. A world where data is clean, columns always have the right names, and a hallucinated answer is just a minor inconvenience. But step into any real analytical environment—insurance, finance, audit, sustainability reporting—and you find the opposite. The data is messy. The rules are unforgiving. And one wrong number can derail an entire deliverable.
In those environments, hallucination isn’t a quirk. It’s failure.
Over the past year, I’ve been building agentic AI systems that interact with serious data: multi-sheet spreadsheets, actuarial tables, CSV directories, complex roll-forwards, and regulatory metrics. And the thing that surprised me most wasn’t how powerful these models are—it was how quickly they can break when you ask them to behave like flawless calculators instead of what they truly are: probabilistic reasoners.
Eventually I realised something simple and slightly uncomfortable: The reliability of an agent has almost nothing to do with the intelligence of the model. It has everything to do with the structure around it.
Here’s what that looks like in practice.
Agents Don’t Fail at Reasoning. They Fail at Boundaries.
When an LLM invents a number, misreads a column, or fills a gap with something “plausible,” it’s not malfunctioning. It’s doing exactly what it was trained to do: produce fluent answers in unbounded spaces.
This becomes obvious the first time you ask an agent to compute something from a spreadsheet and it confidently returns a metric that doesn’t exist anywhere in the file. The model isn’t trying to deceive you. It’s trying to help. But the moment you move from open-ended tasks (“summarise this PDF”) to high-stakes, structured ones (“extract this number precisely”), the mismatch between model behavior and task requirements becomes stark.
The only solution is to narrow the agent’s world until hallucination becomes physically impossible. And narrowing requires architecture.
Reason First, Act Later
The most important shift is to stop treating LLMs as executors. They’re terrible at execution—but excellent at outlining plans.
When I build agents for structured workflows, I always force a separation between reasoning and action. The model is allowed to think, not to do. It outlines the steps it believes it should take, and a deterministic system—usually Python—decides which parts of that plan are valid. If the plan is impossible (“filter column X” when column X doesn’t exist), the model is asked to revise only the broken part.
This simple pattern changes everything. It instantly turns the agent from an improviser into a strategist. And you can audit every decision it made along the way.
Structure Beats Clever Prompting
Another lesson that took me embarrassingly long to learn: an agent becomes dramatically more reliable the moment you constrain the shape of its output.
If the agent must return a JSON object with three fields—and only those three fields—then any attempt to hallucinate something outside that structure is caught immediately. And because LLMs are exceptionally good at self-correction when given constraints, the retry loop usually produces a clean result on the next pass.
This is the point most agent discussions miss.
We don’t reduce hallucination by asking the model to be “more accurate.”
We reduce it by giving the model fewer ways to be wrong.
Domain Logic Belongs in Code, Not in Prompts
In high-stakes environments, domain rules are not polite suggestions—they’re laws. Accounting identities must balance. Loss ratios can’t be negative. Roll-forwards must tie out. But these rules are brittle when embedded in prompts—models follow them until they don’t.
The reliable pattern is to place domain rules outside the model, as deterministic constraints. If the model outputs something that violates the rules, it’s not the user’s problem. The system simply rejects it and asks the agent to try again.
This is the philosophical shift at the core of modern agent design: LLMs produce ideas; code enforces reality. When each does what it’s good at, hallucinations drop to near zero.
Verification Is a Process, Not a Final Step
One of the deepest mistakes is asking an agent for a final answer in one shot. Complex tasks rarely behave well under one-shot reasoning. You wouldn’t ask a junior analyst to perform a full calculation, sanity-check it, write the final summary, and send the client email in a single attempt.
Instead, you would review each stage of their thinking.
A good agent behaves the same way: it plans, it revises, it extracts data, it verifies intermediate results, and only at the very end does it synthesise the narrative. Each step has its own validator. If a single piece fails, only that piece is repeated.
This decomposition is unglamorous, but it’s the real secret to dependable AI.
Build agents the way you’d train a junior analyst
When I think about reliable agents now, I don’t think about neural architectures or parameter counts. I think about people. Specifically: the first junior analyst you ever trained. The one who was brilliant but raw, curious but inconsistent, fast but a little too confident with spreadsheets.
You didn’t hand them a full reporting package and say “good luck.”
You broke things down. You asked them to outline what they planned to do before they did it. You checked their logic. You looked at intermediate numbers, not just the final one. You pointed out where a roll-forward didn’t tie out or where a metric fell outside a realistic range. And you sent them back to revise only the parts that needed revision.
In other words, you created a structure around their intelligence.
Good agents thrive under exactly the same conditions. They don’t need perfect instructions—they need boundaries. They don’t need a 6-page prompt—they need a reviewer. And they don’t collapse under scrutiny; they improve from it. They become clearer, more consistent, more trustworthy, not because the model magically gets smarter but because the system teaches it how to behave.
There’s a quiet elegance in that. It’s not AI as a black box, but AI as a relationship: one part generative, one part grounding, both learning how to meet in the middle.
The Bottom Line: Structure is the antidote to hallucination
When decisions matter—when you’re dealing with financial numbers, operational metrics, sustainability indicators, anything with real-world consequences—you can’t rely on hope or clever prompting. You need architecture. You need constraints. You need the digital equivalent of a steady hand on the shoulder saying: slow down, think clearly, and show your work.
In low-stakes settings, an LLM can afford to be playful. In high-stakes settings, it can’t. And that’s not a limitation—it’s a design principle.
The agents we trust aren’t the ones that guess well.
They’re the ones that think in the open, follow rules without drama, and explain their decisions in ways humans can understand.
In the end, reliability isn’t something the model gives you.
It’s something you build around the model.