AI Agent Reasoning: Reliability and Real Risks

What 'opaque reasoning' in an AI agent really means

When an AI agent presents you with a well-structured response — complete with logical steps, regulatory references and graduated conclusions — what you are seeing is the system's textual output, not its actual computational process. Large language models (LLMs) generate text that resembles reasoning, but the causal chain that produced that response is not the same thing you read on screen.

This phenomenon — known in the technical literature as 'post-hoc rationalisation' — means that a model can construct a coherent justification for an output that was actually generated for entirely different statistical reasons. This is not a malicious intention: it is a structural feature of how these systems work. As Agenda Digitale notes, AI agents that operate autonomously across multiple steps amplify this risk: each step carries its own uncertainty, and uncertainty compounds.

For an accountant, a lawyer or an employment consultant, this creates an immediate practical problem: looking at the output alone, you cannot tell whether the agent genuinely analysed all the documents it was supposed to, whether it correctly weighed the relevant factors, or whether it simply produced a plausible piece of text while omitting crucial information. The output looks correct — the risk lies in the process you cannot see. You can explore this further in our article on AI output control in professional firms.

What you risk as a professional if you do not verify AI output

The question every firm principal must ask is not 'did the AI get it wrong?' but 'can I demonstrate that I checked?' Professional liability in Italy does not transfer to the AI: it rests entirely with the professional who signs the document, the opinion, the declaration.

This means that signing a contract, a legal opinion or a tax analysis note based on unverified AI processing exposes the professional to two categories of risk. The first is technical: the AI agent may have missed a regulatory exception, misread a date, or overlooked a document because it was not correctly indexed in its context. The second is disciplinary and civil: if the client raises a dispute, the absence of a documented process for reviewing AI output significantly weakens the professional's position.

As AI4Business points out, taking an AI agent 'into production' — meaning using it on real cases with direct impact on clients — requires a structured validation phase that many firms skip because the system's interface inspires confidence. A polished visual design, well-formatted text and a professional tone in the output are design choices, not guarantees of accuracy. Our analysis on AI agent liability explores in depth who is accountable when something goes wrong.

What the AI Act says on transparency and explainability of AI decisions

The European AI Act — the regulation already in force with phased application — does not classify AI systems according to how 'intelligently' they reason, but according to what they can do and with what consequences. As Cybersecurity360 emphasises, what matters is not whether the machine 'thinks', but what it can concretely do to the people or organisations affected by its outputs.

For systems classified as high-risk — which include those used in legal, tax, credit-assessment or essential-services contexts — the AI Act sets out precise obligations: technical documentation, operational logs, human oversight, and above all explainability of decisions to end users. An AI agent used by a professional firm to produce opinions or analyses with a direct impact on clients may well fall into this category.

The explainability required by the AI Act does not mean the model must disclose its source code: it means that the user must be able to understand the main factors that determined an output and must be able to challenge it. If your AI agent keeps no accessible logs, does not distinguish the sources it used from the output it produced, and does not allow for tracked human review, it may already be in a non-compliance zone. As Agenda Digitale notes, the current trend in AI systems is towards greater opacity, not greater transparency: more complex architectures (multi-step agents, chain-of-thought reasoning) make it harder, not easier, to explain a decision.

Does your AI agent meet the AI Act's transparency requirements?

• ✕
  Operational logsDoes the system record the steps taken, the documents consulted and the intermediate decisions made?
• ✕
  Source attributionDoes the output clearly distinguish retrieved information from generated elaborations?
• ✕
  Active human oversightIs there a mandatory human review step before the output is used in a signed document?
• ✕
  Right to challengeCan the end user (or the professional's client) be informed that a decision was AI-assisted and request clarification?
• ✕
  Risk classificationHave you assessed whether your specific use case falls into the AI Act's high-risk category?
• ✕
  Technical documentationDoes the AI system provider make available the documentation required by the AI Act for high-risk systems?

How to tell a reliable AI agent from one that produces plausible but wrong outputs

The difference is not visible from the interface. An AI agent that systematically produces incorrect outputs can look exactly the same as one that works correctly. This is perhaps the most counter-intuitive point: the visual and narrative quality of the output is not an indicator of reliability.

To evaluate an AI agent before taking it into production in your firm, you need structured testing on cases with known answers. That means taking 20–30 real situations — contracts already analysed, declarations already checked, questions already resolved — and verifying whether the agent reaches the same correct conclusions. If the agent gets known cases wrong, it is not ready. If the agent cannot indicate what it does not know — in other words, if it does not flag its own contextual limitations — it is particularly dangerous, because it produces confident outputs even under conditions of uncertainty.

A second criterion is traceability of steps: a reliable agent must allow you to follow, at least in broad terms, which document it read, which section it used, and which rule it applied. Without this traceability, you are not using a professional tool: you are consulting an oracle. And oracles, in legal and tax practice, are not admissible as an evidential source. You should also consider integrating AI output management with a structured system such as Ovia Second Brain, which links every piece of information to the right client and context, keeping decisions traceable over time.

The controls your firm must put in place before using AI agents on real cases

AI agent governance is not a technological matter: it is a process matter. The technology may be more or less reliable, but the process surrounding it determines whether its use is professional or risky. This is the principle that guides Ovia's approach: safety does not lie in the AI — it lies in the process.

Concretely, before using an AI agent on real client matters, a professional firm must define at least three elements. The first is the scope of use: which activities the agent may carry out autonomously and which ones require mandatory human review. Not all activities carry the same risk profile: answering a general question about a tax deadline is very different from drafting a contract. The second is the logging system: every interaction with the agent must be recorded, with details of who initiated it, on which matter, with what inputs and what output. Without logs, if a dispute arises you have nothing to show. The third is the escalation procedure: when the agent flags uncertainty, or when the output concerns a high-value or high-risk case, who decides? There must be a named individual — not an implicit assumption.

On the regulatory side, Italian Law 132/2025 has introduced specific disclosure obligations for Italian professionals regarding their use of AI with clients: this is not merely best practice, but a compliance requirement. This makes AI governance a professional-order obligation, not an optional choice. Integrating a system such as Ovia CRM — with deadlines, activity logs and generated documents all tracked by client — allows you to keep everything under control without adding manual work.

Minimum controls before using AI agents in production in your firm

• ✓
  Written scope of useA list of activities the agent may perform autonomously and those requiring mandatory human review.
• ✓
  Active logging systemEvery interaction with the agent is recorded with date, user, matter, input and output.
• ✓
  Human review on high-risk outputsFor signed opinions, contract drafts and tax analyses, human review is a mandatory documented step.
• ✕
  Defined escalation procedureA named individual exists who decides when AI output is unusable or requires in-depth verification.
• ✕
  Updated client disclosureClients are informed of the use of AI tools in the firm's processes, as required by Italian Law 132/2025.
• ✕
  Periodic quality testingThe agent is tested regularly on known cases to verify that its reliability is maintained over time.

AI agent reasoning and reliability: why strategy comes before the tool

The public debate on AI agents tends to focus on model capabilities — how fast they are, how intelligent they seem, how many tokens they process. But for an Italian professional firm or SME, these characteristics are secondary to a simpler question: does this tool, in this process, with these controls, produce outputs I can use with full understanding of what I am relying on?

The answer does not depend on which AI model you choose. It depends on how you integrate it into the workflow, on the controls you activate before and after every output, and on the ability of those running the firm to distinguish the convenience of a tool from the reliability of a process. An AI agent that appears to explain its reasoning but actually simplifies or omits it is not a professional tool: it is a tool that demands an even more attentive professional in order to be used without risk.

The right direction is neither to distrust AI agents outright nor to trust them uncritically. It is to build the process that makes them governable: structured testing, traceable logs, defined human review and a clear scope of use. This is what separates an AI adoption that creates value from one that creates risk. To explore how to set up this approach in your firm, you can also read our article on how to govern AI agent risks and costs.