Wait... What's agentic AI?

New AI buzzwords are popping up all the time, and even the tech-savvy can find them confusing. Terms like AI agents, agentic AI, and compound AI sound similar but mean different things. Understanding their differences and how they are connected will make it easier for you to identify opportunities for workflow automation, as well as sound smart in meetings.

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An AI agent is a software program designed to perceive its environment, process information, and take actions toward a specific goal. In practice, an AI agent might use AI techniques (like LLMs or machine learning) to decide what to do next based on the data it receives. AI Agents are self-contained programs that act on your behalf to carry out a task. If you’re new to AI agents, we recommend starting with our article AI Agent Fundamentals. It offers a clear description of what an AI agent is, what it isn’t, and what they can do.

In simpler terms, an AI agent is like a digital assistant focused on one job. You give it an input or goal, and it figures out the appropriate action within its domain. Unlike traditional software that only does exactly what it’s explicitly programmed to do, an AI agent has a bit of smarts. It can make limited decisions on its own to achieve the goal. Many companies today use AI agents in customer service. For instance, an e-commerce retailer might deploy an AI chatbot agent to automatically handle common customer questions like “Where’s my order?” or to help reset passwords. Unlike a basic FAQ bot, a well-designed AI agent can understand the customer’s request, look up relevant information, and provide an answer without human help.

Agentic AI refers to AI systems with a high degree of autonomy that can handle complex, multi-step problems through advanced reasoning and planning. In essence, agentic AI is a step beyond single-purpose AI agents. It often involves multiple AI agents working together or a single AI that can coordinate various tools and sub-tasks to reach a broader objective.

These systems demonstrate agency, which means they can make decisions, adapt to new information, and act independently across longer workflows, not just in isolated tasks. Instead of simply generating content when asked, it takes the initiative to achieve goals by breaking them into smaller steps and executing them on its own.

Agentic AI is like a proactive AI project manager. You give it an objective, and it figures out what needs to be done, in what order, and then does it. Unlike a single AI agent that might complete one step and then stop, agentic AI can link multiple steps together and adjust if circumstances change. Importantly, it doesn’t rely on a human to trigger every action. It can determine, “If X happens, then I should do Y next.”

For instance, instead of simply answering a question like a typical chatbot, agentic AI might generate follow-up questions for itself or bring in additional tools to solve a more complex problem. Its strength lies in making decisions and taking actions, not just producing output when prompted.
Early examples include autonomous vehicles or smart assistants that manage tasks from start to finish. These systems must constantly observe their environment, make choices, and take action to move toward a goal, all without needing someone to supervise every step. If an AI agent is like a single skilled worker, agentic AI systems are like an autonomous team or a manager you can trust with a project.

One real-world example is IT support automation. A traditional AI agent might classify support tickets or deliver a predefined response. An agentic AI system, on the other hand, can resolve the issue itself. It understands a user's problem written in plain language, checks the necessary systems, identifies the solution, and takes action (such as resetting permissions or passwords) without involving a human.
Another example is a marketing campaign optimizer. You might assign it to manage a digital advertising budget. The agentic AI system would monitor performance data in real time, decide how to reallocate the budget or adjust targeting, and implement those changes automatically to improve results, without waiting for human approval at each step.

In each case, the AI acts like an autonomous assistant that not only understands the goal but also carries out the full process. It manages multiple steps, adapts as it goes, and follows through without needing constant direction.

Compound AI refers to systems that combine multiple AI models, tools, or components to solve a problem more effectively than a single model can. Rather than relying on one large, general-purpose model, a compound AI system brings together specialized parts that work in sequence or in parallel.

Each part is responsible for a specific task. For example, one component might search a knowledge base, another might write a response, and a third might do the necessary math. These components collaborate to create a complete solution, where each part plays to its strength.

When building agentic AI systems, which require the orchestration of multiple steps and decisions, compound AI design becomes especially important. Choosing the right models and methods for each component can significantly improve performance. For instance, using a retrieval model fine-tuned on internal company documents might provide more relevant context than a general web search tool. A smaller, faster language model could be assigned to intermediate reasoning tasks where speed matters more than nuance. Being deliberate about how you assemble these parts helps ensure the overall system is both accurate and responsive.

The main idea behind compound AI is to use multiple focused tools together, often including both AI and traditional software. This approach improves reliability, flexibility, and performance. Many of the most advanced AI systems being built follow this pattern, combining components to achieve more than any single model could deliver on its own.

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Agentic AI is currently the talk of the town. It's being discussed in boardrooms, strategy decks, and AI roadmaps because it promises something beyond simple automation: systems that can manage entire workflows on their own. But with all the buzz, the term often gets muddled or misunderstood.

Hopefully, this article has helped clarify what agentic AI actually involves. You now have a better sense of how AI agents serve as the building blocks, handling specific, contained tasks with limited autonomy. You've also seen how agentic AI ties everything together, orchestrating those agents to pursue broader objectives with independence and adaptability. And you've learned how compound AI plays a supporting role by ensuring that each part of the system is purpose-built, using the right model or tool for each specific task.

Understanding how these three ideas fit together gives you a clearer lens through which to evaluate or design intelligent systems. Agentic AI may be the headline, but it depends on capable agents and careful system composition. As your team begins exploring use cases or prototyping solutions, keep in mind that success often comes not from building one powerful model, but from assembling the right combination of smaller, specialized ones that can work in concert.

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