AI Agent Errors: Lack of Data or Poorly Written Mission?

Your AI agent answers off-topic, forgets information, or produces incoherent results. Before concluding that it is defective, ask yourself a simple question: did you give it what it needed to succeed? In most cases, errors come from two distinct sources. Confusing them means wasting time correcting the wrong problem.

IWhy does an AI agent make mistakes?

An AI agent does not invent its answers from nothing. It relies on two elements: the documents and information you have provided it, and the mission you have written for it. When one of the two is insufficient, the agent does what it can with what it has. The result looks like an error, but it is often a logical response to an incomplete situation.

Let's take a concrete example. An electrician tradesperson asks their agent to draft quotes. The agent proposes incoherent prices. First hypothesis: the tradesperson did not provide their pricing grid. Second hypothesis: the mission simply says "draft quotes" without specifying how to calculate amounts. In the first case, information is missing. In the second, the agent does not know what to do with the information even if they had it.

IIHow to recognize a data shortage?

The typical symptom: the agent gives vague, generic answers, or invents details that don't exist. It fills the gaps with what it assumes, for lack of anything better. If you ask why it wrote something, it cannot cite a precise source in your documents.

To diagnose this problem, open your agent's file and ask yourself this question: if a new employee read only these documents, could they do the job? If the answer is no, your agent lacks material. This is not a tool defect, it's an incomplete file.

IIIHow to recognize a poorly written mission?

The symptom is different: the agent does something, but not what you expected. It has the information, it uses it, but in a direction that surprises you. Or it constantly asks you the same questions, unable to decide on its own.

Do this test: show your mission to someone who does not know your profession. Ask them to explain to you what they understand. If they hesitate, rephrase, or ask questions, your mission lacks clarity. The AI agent reacts the same way a human does when faced with a vague instruction: they interpret, and their interpretation may differ from yours.

IVThe three-step diagnostic method

VEnrich the context: which documents to add?

When diagnosis points to a lack of data, the solution is to enrich the agent's file. But be careful not to throw everything in haphazardly. An agent who receives too many poorly organized documents can get lost just as easily as an agent who doesn't have enough.

An architecture firm had provided its agent with all of its past projects, totaling hundreds of pages. The agent got lost in this mass of information. By reducing the file to the three most representative projects and a sheet summarizing typical services, the answers became precise. Quality over quantity.

VIRephrase the mission: points to clarify

If the problem comes from the mission, it must be rewritten with more precision. A good mission answers five questions: what must the agent do, for whom, in what format, with what limits, and what to do in case of doubt.

The difference between the two columns is that the precise mission leaves no room for interpretation. The agent knows exactly what to do, even in edge cases. Each ambiguity removed is an error avoided.

VIIWhen both causes combine

Sometimes the problem comes from both sides. The agent lacks a document, and the mission doesn't tell it how to react to this lack. Result: it improvises poorly. The solution is then twofold: add the missing information, and clarify in the mission what the agent should do when it cannot find a piece of data.

I often see users who enrich the file without touching the mission, or vice versa. The best results come from work on both fronts. A complete file with a vague mission remains problematic. A perfect mission without the right documents produces nothing useful.

VIIIImprove gradually rather than start over

When faced with an agent that makes mistakes, the temptation is to erase everything and start over. This is rarely the right approach. Each error is information about what is missing. By correcting point by point, you build an increasingly reliable agent, adapted to your business reality.

On ABYS, the blank test allows precisely this iterative work without risk. Nothing is sent until you have validated it. Take advantage of this possibility to test your corrections before deploying them. An agent well calibrated after three or four adjustments is better than an agent launched too quickly that accumulates errors.