Agentic AI vs. AI Agents: How to Differentiate 

The Greeks have eight (or six, four, or three, depending on who you ask) definitions for love because distinguishing the types of love has long been important to how they see the world. People say that the Eskimo-Aleut language has 50 (or 300 or 1,000 depending on your cliché user) for snow, also reflecting how important the concept is to their everyday lives.

We also see this in the artificial intelligence (AI) space — terms that denote specific functionality are evolving to reflect real technical abilities and how they impact our world. Three, in particular, are similar enough that they have great potential to confuse: generative AI (gen AI), AI agents (intelligent agents), and agentic AI.

We can think of the three as a continuum: Gen AI often is at the heart of AI agents, and AI agents often make up agentic AI systems. 

Let’s talk about them a bit and highlight the strengths of each.

What Generative AI Is

Generative AI refers to a class of machine-learning models designed to produce content—such as text, images, audio or video—by learning patterns from existing data and then sampling from that learned distribution. These systems include language models (e.g., GPT), diffusion or GAN-based image generators, and other architectures capable of producing generated outputs.

How you ask a question may, and often will, change the response you get. Think of it like this: If you put cream, eggs, sugar, and vanilla together, you can make ice cream, custard, gelato, or crème brulee. How you put them together and in what quantities changes your outcome. 

Generative AI models excel at scanning vast amounts of data in milliseconds and producing plausible responses by leveraging statistical patterns learned during training. They don’t “solve” problems in the human sense; instead, they perform probabilistic inference over the information they’ve been exposed to. 

While techniques like chain-of-thought prompting can coax LLMs into approximating multi-step reasoning and synthesizing novel combinations of concepts, their outputs remain ultimately bound by their training data and can still hallucinate or break down on complex logic. 

In short, their strength lies in high-speed pattern matching and generalization, not in genuine understanding.

AI Agents Are Good at Performing Specific Assigned Actions 

AI agents and agentic AI often are described using the same language. This likely grew from the fact that AI agents or intelligent agents have been in wide use for more than 20 years, but they haven’t accomplished what we originally intended, which is the ability to learn and adapt over broad domains.

What we see being described as AI agents these days are programs that can perform actions independently. To be clear, someone programmed the agent with parameters and triggers, and it follows those prompts, mostly with very little human guidance. AI agents do have the ability to change course, depending on programmed inputs. They are widely used for workflow automation.

AI agents “can carry out complex tasks, but still only specific tasks they were created for. They can’t really apply their learning to doing other things in the same way humans can,” writes Bernard Marr, author of Generative AI in Practice: 100+ Amazing Ways Generative Artificial Intelligence is Changing Business and Society.

AI agents are in use in a handful of fields, including fintech, education, and customer service.

Agentic AI Plans and Iteratively Executes Towards a Goal

When provided with a specific goal, Agentic AI systematically formulates a plan and iteratively executes tasks to achieve it. The system continuously loops through multiple actions, using the outcomes of previous tasks to dynamically refine and adjust subsequent steps. Agentic AI operates with defined agency, acting autonomously within the constraints and contexts you set, such as interacting directly with systems like a knowledge base or CRM, to progress effectively toward its assigned objective.

As such, an agentic system may call on multiple AI agents, apps, or workflows programmed to do different things, including:

• retrieve data, 
• summarize that data, 
• create a support ticket, 
• construct an email, 
• generate a chart or image, or
• send an email.

If you look at Google’s description of AI agents, it reads a lot like Nvidia’s description of agentic AI, so keep in mind the overlap in language as you read through any of the links in this post. You may not want to apply these distinctions, but this will give you a starting point for figuring out the language others are using.

Something that gen AI and AI agents have in common is that they are both limited by their inputs:

• for gen AI, what their designers used to train them on
• for AI agents, what they were designed to achieve, plus their training materials

In other words, these two technologies are limited by what someone thought to tell them.

The hope is that in the near future, agentic AI will overcome these limitations as it becomes more able to:

• Make decisions based on what it understands from its inputs
• Adapt to new information and changing circumstances 
• Learn from experience
• Solve complex problems that require multiple steps
• Use all other forms of technology it can access and incorporate those strengths

When it matures, agentic AI will use all of the above to achieve specific objectives.

One of the most visible agentic AI applications is self-driving cars, which make driving decisions while responding to traffic and navigating. This is a perfect example to describe the field: It is still aspirational, but we continue to make strides toward a true agentic AI.

Why Does the Distinction Between Agentic AI and AI Agents Matter?

Agentic AI has the potential to deliver real, measurable value by taking on complex, evolving tasks that traditional automation just can’t handle. It can help teams work more efficiently, speed up decision-making, and deliver smarter, more personalized experiences for customers. 

But these benefits don’t come automatically. They require thoughtful investment — in advanced models, the right infrastructure, and a clear understanding of where AI fits into the bigger picture. Leaders need to balance those upfront costs with the long-term value AI can unlock across the organization.

Design & Governance

As AI systems become more autonomous, strong governance becomes non-negotiable. Agentic systems — those that act on their own to achieve set goals — need clear ethical guidelines, transparency through audit logs, and the ability for humans to step in when needed. This isn’t just about checking a compliance box; it’s about building and maintaining trust — with your customers, regulators, and your own teams — as AI becomes a bigger part of how your business operates.

Risk Profile

Giving AI more autonomy also raises the stakes. Without the right checks, these systems can veer off course — making decisions that don’t align with your strategy or that carry legal and reputational risks. That’s why ongoing monitoring, alignment with business objectives, and the ability to intervene quickly are so important. Managing this risk isn’t just a safety net — it’s a core part of ensuring AI adds value in the right ways, without compromising what matters most.

LLMs Help Realize the Potential of Agentic AI

Although research in intelligent agents has been active for 25 years or more, in practice intelligent agents have not found wide applicability, because the agents that work acceptably well are very specialized in terms of their domain knowledge and decision-making capability. 

The hope with agentic AI is to expand the applicability of intelligent agents by including an LLM as a general reasoning and decision support core component in an intelligent agent system.

It follows, then, that making AI agents and agentic AI work for the enterprise means adding specific domain knowledge to your LLMs.

Leveraging Retrieval Augmented Generation

Enterprises bring verified organizational knowledge into an LLM using Retrieval Augmented Generation, or RAG, which helps mitigate AI’s biggest challenges by retrieving real, relevant data from inside and outside of the enterprise.  “RAG helps large language models (LLMs) deliver more relevant responses at a higher quality”, according to IBM.

In other words, it fills a gap in how LLMs work by using your own data.

In a nutshell, RAG frameworks traditionally follow three steps:

1. Retrieval: The retrieval model searches data to identify the most relevant documents or text chunks related to the user query.
2. Augmentation: The retrieved information is then combined with the original query to create an enriched prompt that provides context for the generative model.
3. Generation: The generative model uses the enriched prompt to create a response that incorporates the relevant information from the retrieved data. 

With RAG, the ‘retrieval’ step is both the most important but also the most difficult. RAG systems encounter the same enterprise challenges that more traditional information retrieval systems do, like: 

• Data scattered across many places
• Data stored in many formats
• Data serving many functions
• Data that lacks context
• Data that is outdated or of poor quality

Retrieval is the foundation of RAG, and it’s also the hardest part to get right — especially for enterprises. Many organizations assume RAG is a one-size-fits-all solution, but the reality is that most retrieval methods and techniques struggle to handle the complexity of enterprise content at scale.

Coveo’s Advanced Retrieval Approach 

To meet enterprise-scale RAG demands, Coveo’s approach prioritizes precision, security, and scalability, using:

• A two-stage retrieval process: First, AI identifies the top most relevant documents. Then, it extracts only the most relevant text passages, their ranking score, links to the source content for citations, and appropriate metadata, then injects that information into the original query to form the updated prompt for the generative model — minimizing noise and maximizing accuracy.

• Hybrid ranking: Combining semantic (vector) search, and lexical (keyword) matching ensures Coveo retrieves the right information in the right context. 

• ML-Powered Relevancy: AI continuously learns from user interactions, tailoring retrieval to each user’s journey, behavior, and profile for context-aware responses.

• Unified Index: A centralized hybrid index that connects structured and unstructured content across all sources. Coveo provides a robust library of pre-built software connectors to maintain seamless integration with third-party platforms (Salesforce, SharePoint, Zendesk, etc.), ensuring data stays fresh with automatic updates for real-time retrieval.

• Analytics & Insights: Improve generated answer quality, identify missing or underutilized information, and maximize business impact with visibility into answer success rates, content performance, gaps, and optimization needs.

• Keep data secure by generating answers only from content users are allowed to see. Respects document-level permissions, using early-binding security.

In Summary

Not all AI agents are created equal. While every Agentic AI system is technically an AI agent, the reverse isn’t true — and the distinction matters.

“AI agent” is a broad term that describes the architecture: systems designed to act on instructions. “Agentic AI,” on the other hand, goes a step further. It refers to systems with a higher level of independence — ones that can plan, adapt, and take initiative based on goals, not just follow predefined tasks.              

If you’re evaluating or designing a solution, ask yourself:

• Is the system simply carrying out user-defined steps? That’s an AI agent.
• Is it identifying its own objectives and figuring out how to achieve them within set boundaries? That’s Agentic AI.

Understanding where a system falls on this spectrum helps you make better choices—from which technologies to use, to what guardrails to set, to how you’ll measure success. It’s the foundation for building AI that’s not just smart, but strategic.