Since the emergence of generative artificial intelligence, organizations have discovered tools capable of writing, summarizing, translating, and coding at impressive speeds. But a new generation of technologies is on the horizon: Agentic AI. Here, the goal is no longer just to produce content or answer a request, but to delegate an entire mission to an autonomous system — including planning, execution, and adjustment.
Why Agentic AI Is Not Just Another Kind of AI
This technological leap opens up promising prospects: increased productivity, intellectual scalability, and the automation of complex business processes. But like any major disruption, it is accompanied by profound risks. And the more autonomous the agents are, the more side effects can become systemic.
This article offers a detailed analysis of the key risks associated with Agentic AI, the traps to avoid for decision-makers and CIOs, as well as best practices for combining technological power with strategic control.
Understanding What Agentic AI Is, and What It Is Not
Before discussing the risks, it is essential to clarify what Agentic AI truly encompasses. Unlike classic generative AI, which is often limited to responding to a specific, one-off instruction, agentics introduces a new logic: that of the mission rather than the simple task.
Agentic AI is characterized by several fundamental capabilities:
- Understanding an objective formulated in natural language, without requiring ultra-precise or coded instructions.
- Planning a coherent sequence of actions to achieve this objective, taking into account context and constraints.
- Acting autonomously by mobilizing different tools, APIs, or databases, without relying on constant human guidance.
- Self-evaluating and adjusting its trajectory, by detecting its own errors or limitations and correcting its behavior.
- Collaborating with other agents or with humans, to function as a link in a network rather than as an isolated tool.
The key difference with classic AI assistants therefore lies in the operational mode: where copilots and chatbots remain reactive and guided step-by-step, Agentic AI adopts a proactive approach, focused on a final objective.
But this autonomy, while paving the way for powerful applications, also raises new risks. Without rigorous supervision, an agent can drift, make decisions outside the planned scope, or interact unpredictably with its environment. This is why governance and human oversight must remain at the heart of any agentic strategy.
The Risk of Hallucinations: Invisible Errors with Heavy Consequences
Hallucinations are a well-known phenomenon to language model users: they are false or invented statements, often formulated with great confidence. In the context of Agentic AI, this risk takes on a new and much more worrying dimension.
Indeed, the agent does not limit itself to providing an isolated answer: it constructs a sequence of actions based on its assumptions. If one of these assumptions is wrong from the start, the entire mission can be compromised. A hallucination upstream acts as a foundational error, contaminating the entire downstream process.
Concrete Cases of Hallucinations in a Professional Context
- An agent produces a competitive benchmark citing fictitious sources.
- It generates a legal report based on non-existent case law.
- It makes an operational decision based on misinterpreted data.
These drifts, sometimes difficult to detect immediately, can have heavy consequences: loss of credibility, strategic errors, or even legal liabilities for the organization.
The Most Frequent Causes
- Use of an unverified corpus (unreliable web pages, unstructured or obsolete content).
- Imprecise or poorly configured instructions, which leave too much room for the model's interpretation.
- Absence of intermediate quality control, allowing errors to pass that could have been corrected earlier.
Best Practices for Limiting Hallucinations
- Rely on reliable business corpora, derived from validated internal documents, regulatory databases, or recognized scientific publications.
- Implement automated verification by control agents or cross-reviews, capable of detecting and signaling inconsistencies.
- Maintain systematic human oversight over critical deliverables, to guarantee the reliability of the final outputs.
In summary, hallucinations are not just an anecdotal flaw: they represent a structural failure if they are not anticipated. In an agentic system, the rigor of the sources and the establishment of control loops therefore become essential conditions.
Poorly Framed Autonomy: When the Agent Acts Without Understanding the Context
Autonomy is the promise of Agentic AI. But poorly framed, it becomes a danger. Without a clear framework, an agent can:
- Take too many initiatives, beyond what was expected.
- Step outside its scope of competence, venturing into decisions that exceed its area of expertise.
- Act without business coherence, or even against internal rules and established procedures.
The real danger therefore lies not in autonomy itself, but in the ambiguity of the contract between the human and the agent. When responsibilities, limits, and control mechanisms are not explicitly defined, the agent can drift and compromise the reliability of the system.
Two Forms of Autonomy to Differentiate
- Execution Autonomy: the agent acts within a well-defined framework, with precise rules. Its room for maneuver is limited, and its actions are always aligned with objectives set by the human.
- Decision Autonomy: the agent is capable of reformulating objectives or prioritizing its actions without direct supervision. It is this level which, if not strictly supervised, can lead to dangerous drifts.
Examples of Observed Drifts
- An agent modifies the order of steps in a quality process without consulting anyone, compromising the compliance of the deliverable.
- An agent misinterprets an HR rule, generating an error with legal consequences for the organization.
Best Practices for Framing Autonomy
- Define a strict functional perimeter, clearly specifying what the agent can do and what remains the exclusive responsibility of the human.
- Implement regular supervision with validation of deliverables by a business expert, to maintain constant control over outputs.
- Favor "white box" architectures, allowing the agent's reasoning to be audited and its choices to be explained in case of doubt or incident.
Clearly, the autonomy of Agentic AI must not be endured, but governed. Well-framed, it becomes a lever for performance; poorly defined, it transforms into a risk factor.
Hidden Costs: The Illusion of "Free" AI
At first glance, Agentic AI seems irresistible: a quick-to-deploy tool, inexpensive to run, and immediately productive. But in practice, this image quickly fades. Hidden behind the apparent autonomy of agents are indirect costs that many companies underestimate or even ignore.
Types of Costs to Monitor
- Initial configuration time
The more generic an agent is, the more effort it requires to be adapted to the business logic, sector-specific exceptions, and formats expected by users. This initial setup, often long and tedious, can delay adoption. - Correction cost
A poorly framed or incomplete deliverable leads to time-consuming back-and-forths: successive adjustments, time wasted on validation, or even complete rejection of the work produced. The illusion of speed quickly disappears if quality is not met from the first iteration. - Loss of team confidence
An AI that is wrong too often, even if it is technically competent, is quickly abandoned by its users. Organizational distrust is then an intangible but heavy cost, as it slows down adoption and reduces return on investment. - Invisible technical cost
Many agents rely on remote models via API calls. If poorly optimized, these flows can generate significant surcharges at scale, especially when query volumes explode.
Best Practices for Limiting These Drifts
- Prioritize configurable agents, capable of finely integrating business ontology from the start, to reduce initial friction and increase relevance.
- Establish a user feedback system, to refine responses over time and progressively reinforce agent reliability.
- Measure costs per deliverable, and not just by the volume of API calls or tokens consumed. The evaluation must focus on the value created (a document, a synthesis note, a recommendation) rather than isolated technical metrics.
In summary, Agentic AI is not free. It requires a thoughtful investment in configuration, supervision, and optimization. This is the price at which it can deliver on its promise of productivity and avoid becoming an invisible drain on the organization.
Loss of Human Control: The Black Box Syndrome
Among the risks associated with Agentic AI, this one is arguably the most insidious: the illusion of control. When the results produced by an agent seem correct, but no one can explain how or why they were obtained, the organization shifts into a fragile and dangerous management mode.
Potential Consequences
- Dilution of Responsibility
If an autonomous agent makes a bad decision, who should bear the consequences? The tool that produced the action, the developer who configured it, or the end-user who validated the deliverable? The lack of clear traceability makes governance uncertain. - Impoverishment of Internal Skills
When humans limit themselves to the role of simple validators, they gradually lose their critical analysis and interpretation capacity. Eventually, teams become dependent on the machine and less able to detect errors or challenge results. - Strategic Risk
An AI that always reasons in a standardized way ultimately standardizes thinking. However, a company's competitive advantage often relies on nuance, intuition, and contextual reading — qualities that AI cannot replicate without human intervention.
Best Practices for Maintaining Human Control
- Train users to interact intelligently with the AI. The quality of a response often depends on the relevance of the question asked, hence the importance of learning how to formulate and reformulate effective prompts.
- Encourage human reformulation of deliverables. The AI can propose a first version, but the human must retain control over the final adjustment, adding their judgment, creativity, and business insight.
- Require transparent and auditable agents. Each step of the reasoning must be explainable, documented, and, if necessary, contestable. So-called "white box" architectures offer this guarantee of explainability and strengthen trust in the AI.
Ultimately, the value of Agentic AI lies not only in its automation power but in its ability to strengthen — and not weaken — the cognitive sovereignty of the human.
The Absence of Governance: The Achilles' Heel of Agentics
In discussions surrounding Agentic AI, the focus is often placed on autonomy. But an equally crucial element is sometimes neglected: governance. An Agentic AI can be high-performing, efficient, and fast; yet without a solid governance framework, it becomes a threat — to regulatory compliance, reputation, and the organization's very resilience.
Risks Related to Absent or Weak Governance
- GDPR Non-compliance. Poorly processed personal data, lack of consent, or inappropriate storage of sensitive information: all vulnerabilities that expose the company to sanctions.
- Auditability Difficulties. Without clear rules, it becomes impossible to trace a decision, understand the origin of an error, or explain a result to a regulator or client.
- Legal Exposure. A decision made by an autonomous agent, if not supervised, can directly engage the company's responsibility. The financial and reputational consequences can be severe.
Key Components of Solid AI Governance
- A clear supervision policy: who validates what, when, and with what quality criteria?
- Integrated explainability mechanisms, allowing the steps of the reasoning to be understood and each choice to be justified.
- Continuous monitoring of performance, errors, and behavioral drifts, to adjust the system in real-time and prevent incidents.
Example: DigitalKin's "Agentic Mesh" Approach
DigitalKin proposes an Agentic Mesh architecture where several specialized agents collaborate... and correct each other. Every action is tracked, every deliverable is linked to its sources, guaranteeing complete auditability. And above all, the human retains the final say, in an assumed logic of sovereignty and transparency.
In short, autonomy without governance is a risk, but well-governed autonomy is a strategic opportunity.
Why Transparency Is the Best Safeguard
In the age of Agentic AI, transparency can no longer be considered a simple bonus; it must become an absolute prerequisite. Without it, no lasting trust can be established between users, regulators, and systems. It conditions not only security and compliance but also adoption and, ultimately, the performance of the agents.
What Transparency Must Cover
- The data sources used by the agent, so that every statement can be verified and linked to a reliable corpus.
- The reasoning followed to reach a result, with explicit and understandable logic for a business expert.
- The limits of the agent, clearly displayed to avoid unrealistic expectations and recall the areas in which the AI is not reliable.
- The role of the human in the decision loop, as human supervision remains a pillar of governance and accountability.
In practice, transparency is defined as an equation: intelligibility + traceability + accountability. A well-designed Agentic AI does not need to maintain mystery. On the contrary, it becomes more effective when it is understood, because users can then dialogue with it critically, detect potential errors, and strengthen its value.
By making every step visible, documented, and contestable, transparency proves to be the best safeguard against drifts and the foundation of the trust essential for large-scale adoption.
FAQs — The Risks of Agentic AI: What Every Decision-Maker Must Know
- Is Agentic AI riskier than classic Generative AI?
Yes. Where Generative AI is limited to answering a specific request, Agentic AI is capable of planning and executing actions autonomously. This autonomy increases the potential scope of errors: an isolated hallucination becomes a chain decision, with much heavier consequences. - Can an AI agent be trusted in a regulated context?
Yes, provided that a governable architecture is deployed: systematic human validation, complete traceability of decisions, and integrated supervision mechanisms. Without these safeguards, compliance cannot be guaranteed. - Do you need AI experts to supervise agents?
Not necessarily. It is primarily business experts who play a key role. The challenge is to train them to collaborate with the agent and correctly interpret its outputs, rather than demanding sharp technical skills in AI. - Are hallucinations 100% avoidable?
No. No architecture can completely eliminate them. However, it is possible to drastically reduce them through the use of reliable sources, cross-supervision mechanisms, and human validation of critical deliverables. - How is the true cost of an AI agent calculated?
The cost is not measured solely by the number of API calls or tokens consumed. It must evaluate the value of the final deliverable, the time actually saved (or lost) by the teams, as well as the technical and organizational costs associated with the usage. - Can Agentic AI replace an employee?
No. It can automate repetitive tasks and accelerate certain analysis steps, but it does not replace critical judgment, contextual discernment, or human creativity. Its role is to augment the human, not to supplant them.
Conclusion: Mastering Agentic AI Means Mastering Your Transformation
Agentic AI should not be feared. It must be framed with rigor. Its transformative power is undeniable, but it requires heightened vigilance. To get the best out of it, three conditions are essential:
- A rigorous design, anchored in solid business frameworks and focused on usable deliverables.
- Strong governance, ensuring supervision, explainability, and shared responsibility.
- A culture of human-machine dialogue, where AI is not a substitute but a partner in thought and action.
Agentic AI is not just another technical innovation. It constitutes a profound reconfiguration of how we think, decide, and produce. It redefines the relationship between human and technology, between delegation and sovereignty, between speed and reliability.
And it is precisely because it is promising that it deserves responsible, transparent, and fully controlled integration. The question is therefore not whether Agentic AI will transform organizations, but how we choose to govern it so that it serves our strategic, human, and societal objectives.