In the field of AI, the notions of “AI agent” and “agentic AI” are increasingly mentioned, often presented as equivalent even though they refer to two distinct concepts.
As these technologies evolve, clarifying this distinction becomes crucial: it is an essential condition for designing appropriate governance frameworks, managing emerging risks, and steering innovation responsibly.
1. Definitions of the Terms
AI Agents
AI agents refer, in their usual meaning, to AI software systems with specific characteristics:
- They rely on an AI model pursuing a defined or undefined goal, without having undergone additional development or significant modification
- They are accessible through a studio in which the user can edit their parameters
- They are configured with the aim of automating a complex, contextualized, making decisions and executing actions without necessarily relying on human intervention
Example: An AI agent may be an automated assistant responsible for sorting incoming emails. It analyzes each message, identifies its category (sales, support, urgent), then applies the appropriate action, such as archiving or creating a ticket. It thus accomplishes a precise, predefined task without exceeding the scope of that role.
Agentic AI
Agentic AI extends and surpasses this framework. It refers to the notion of agency, that is, a system’s ability to:
- Act autonomously
- Initiate actions
- Plan sequences
- Adapt to changing contexts
- Pursue high-level goals without continuous human supervision
According to the taxonomy proposed by Sapkota et al. (2025), agentic AI represents a paradigm shift compared to traditional AI agents. It is notably characterized by:
- Collaboration among multiple agents within the same system
- Dynamic decomposition of tasks into context-appropriate subtasks
- The existence of persistent memory allowing long-term use of historical information
- Orchestrated autonomy, that is, structured and coordinated, going beyond the capabilities of an isolated agent
It is therefore a global system endowed with autonomy and coordination capacities.
Example: In a multi-agent system, each agent executes a specific subtask to reach the objective, and their efforts are coordinated through AI orchestration features.
Relationship Between the Two Notions
We can therefore say that agentic AI always includes AI agents, but not all AI agents are part of agentic systems. In other words:
- AI agents constitute the foundation: they automate specific tasks using intelligent modules
- Agentic AI represents an evolved form of that foundation, where several agents interact, cooperate, coordinate, and manage more complex and long-term objectives
| And what about Chatbots? A chatbot (or conversational assistant) is an AI system designed to simulate a conversation in a given channel and provide information, assistance, or a service. It can answer FAQs, check the status of an order, recommend a product, or guide a user through a form. Unlike an agent, a classic chatbot does not pursue any objective, does not plan a strategy, does not reason in multiple steps, and limits itself to responding to incoming messages. It does not use an internal chain of thought nor deep adaptation to the business context. It is a conversational AI system, but not an agent. |
2. Autonomy, Purpose, and Decision-Making
One of the key differences lies in the nature of the goal and decision-making.
2.1 Goals and Objectives
- AI agents are task-oriented: they execute what they are programmed for, often with well-defined conditions. They have limited initiative.
Example: an AI agent programmed to fill out a predefined form
- Agentic AI is goal-oriented: it can receive a longer-term or more general objective and determine how to achieve it, often by breaking down the objective into sub-objectives and adapting its plan.
Example: an agentic AI system capable of managing an entire workflow to optimize the claims-management process in an insurance company
2.2 Decision-Making and Learning
- AI agents: AI agents have limited decision-making abilities, often relying on rule-based systems, predefined action flows, or prompts combined with integrated tools. Their learning is generally external, requiring reprogramming or manual updates to modify their behavior.
Example: an AI agent receives a customer request stating they forgot their password. The agent analyzes the message, identifies that it is an account-access issue, verifies that the user has a valid email, then decides to automatically send the reset link.
- Agentic AI: Agentic AI is characterized by more complex and dynamic decision-making, with the ability to learn and adapt over time. It is equipped with more persistent memory as well as collaboration capabilities among agents and orchestration, meaning the coordination of multiple agents to accomplish more sophisticated tasks.
Example: an agentic AI used in cybersecurity could detect an emerging attack, adjust filtering rules in real time, deploy countermeasures, reassess system exposure, and adapt its behavior based on the evolving threat, without immediate human intervention.
2.3 Autonomy
- Relative autonomy for AI agents: the environment is relatively stable, interactions predefined.
Example: a support agent analyzes a user’s question, selects the appropriate answer from a predefined database, and delivers it automatically.
- High autonomy for agentic AI: the environment is dynamic, the system can modify its own plan, interact with other agents, and even create new sub-agents or modules to achieve its objectives.
Example: in an industrial company, a main agent could orchestrate one agent analyzing maintenance data, another planning production, and a third managing procurement, in order to simultaneously optimize schedules, inventory, and preventive maintenance.
| Comparative example: AI Agent vs. Agentic AI In a company, an AI agent could automate generating an invoice based on a template, while an agentic AI system could detect a billing anomaly, trigger an investigation, reconfigure the process, alert stakeholders, and learn from the error to prevent it from reoccurring. |
3. Architectures and Technical Characteristics of AI Agents vs. Agentic AI
Here is a simplified comparative table of the two paradigms:
| Criterion | AI Agents | Agentic AI |
|---|---|---|
| Field of action | Well-defined tasks, relatively autonomous modules | High-level objectives, multi-step workflows |
| Autonomy | Within the limits of its assignment; follows predefined rules/flows | High autonomy: planning, adaptation, learning |
| Memory / context | Often limited per session or per task | Persistent memory, long-term context, multiple interactions |
| Multi-agent collaboration | Can be isolated or weakly coordinated | System of collaborative agents, orchestration, inter-agent exchanges |
| Typical example | Chatbot, automated desktop assistant | Platform that decomposes a goal, creates sub-agents, learns and adjusts |
| Architecture | Integration of an LLM + tool or predefined action flows | Modules of perception + reasoning + planning + execution + memory + minimal supervision |
4. Issues, Limits, and Challenges
The evolutions of AI, whether in the case of an AI agent or agentic AI, give rise to multiple challenges:
Risks of coordination and emergent behaviors:
In agentic systems, collaboration between several agents can trigger unforeseen interactions, generating cascade effects or chain reactions that are difficult to anticipate. These emergent behaviors can impact the execution process and lead to divergences between the expected objective and the action actually carried out.
Responsibility and human control:
The more autonomous and complex a system is, the more difficult it becomes to clearly identify the source of a decision, assign responsibilities, or ensure compliance and security. Agentic systems amplify this challenge, whereas in more traditional systems, humans generally retain an explicit control capability at each stage of the lifecycle.
Transparency and explainability:
These systems, based on opaque models, often resemble “black boxes”: it becomes complex to trace their reasoning, understand why a decision was made, or verify the consistency of intermediate steps. The opacity limits the ability to audit, document, or justify the system’s behavior.
Governance, alignment, and bias:
As with any AI technology, it is essential to implement robust governance practices: alignment of objectives, bias management, human oversight mechanisms, and reinforced security measures, especially in agentic systems where multi-agent interactions increase complexity and the risks of deviation.
The difference between an AI agent and agentic AI is significant: the former remains an automated tool with limited capabilities, while the latter refers to autonomous, coordinated systems capable of pursuing complex objectives in an adaptive manner.
The literacy of these notions is still developing, hence the importance of relying on clear definitions and remaining attentive to sometimes blurry terminology.
For organizations, this means determining the expected level of autonomy, coordination, memory, and complexity in order to choose the appropriate architecture and anticipate associated governance requirements.
Would you like to better understand agentic AI, structure your governance, or anticipate upcoming regulatory frameworks?