The 5 Levels of Agentic AI Systems

We've all chatted with artificial intelligence (AI), maybe asked it to write a poem or summarize a long email. That's neat, but it's like watching a car and only admiring its paint job. The real story with today's artificial intelligence is under the hood, where these systems are learning to do more than just respond. AI isn't just spitting out text anymore. It can think, plan, and do on its own. They're starting to make choices, use tools, and even manage complex tasks on their own.

The way large language models (LLMs) are being used has changed as these systems can now take on roles that used to require a full human brain (or a team of them). This isn't about AI just thinking; it's about AI acting. If you've been hearing terms like "agentic AI" and wondering what the hype's about, this is it. Agentic AI are systems with a degree of autonomy that allows them to perform actions.

In this guide, you'll learn about the 5 levels of agentic AI systems. Agentic systems are smart; they go from being passive assistants to fully active agents that make decisions, call functions, write and run code, and even manage other agents. However, not all AI agents are created equal. Some still need their hand-held, while others might just ignore you while building an app from scratch. Their ability to act independently falls on a spectrum, a kind of ladder of capability.

Let's climb that ladder!

Here's a clear breakdown of the 5 levels of agentic AI systems—what they are, how they work, and why they matter:

Level 1: Basic Responder

This is your classic prompt-in, text-out setup where AI acts like a straightforward tool, responding directly to human instructions within a process entirely controlled by a person. The LLM responds to what you give it and stops there. It's reactive, not proactive. Think ChatGPT, Gemini, Claude, and other AI chatbots in default mode.

• A human guides the entire flow from start to finish.

• The Large Language Model (LLM) functions as a generic responder.

• It receives an input and then produces an output.

• The AI has very little say or control over the program's direction.

• The human user makes all significant decisions.

Level 2: Router Pattern

At this stage, the AI models can make small decisions and get a bit more responsibility, choosing from predefined paths or functions that a human has already set up. You still define what options exist, but the LLM decides between them, like a GPS that doesn't control the car but can suggest the fastest route.

• A human defines the available paths or functions within the workflow.

• The LLM makes elementary decisions about which function or path it should take.

• The overall design of the flow is still in human hands.

• The AI selects its course of action from a limited menu of options.

• There's a noticeable, albeit small, increase in AI involvement in the process.

Level 3: Tool Calling

Here, the AI models get smarter and can pick and use specific tools, provided by humans, to complete a task, figuring out on their own when to use them and how. Users can give it access to a toolkit—it decides which tool to use, when to use it, and how. It can pull in live data, interact with APIs, and more.

• A human defines a specific set of tools that the LLM can access.

• The LLM decides when it's appropriate to use these tools.

• It also determines the necessary arguments or parameters for the tools' execution.

• The AI takes on a more active part in accomplishing the task.

• Think of it as a human providing a toolbox, and the AI selects the right tool for the job.

Level 4: Multi-Agent Pattern

This level where it starts feeling like a small AI company with the introduction of a team of AIs, where a designated agent acting as a manager coordinates and directs multiple sub-agents and repeatedly decides on the next steps to solve more complex jobs. Each sub-AI agent has a role and possibly its own tools.

• A manager agent is tasked with coordinating several sub-agents.

• This manager agent determines the subsequent steps in an iterative fashion.

• A human designs the hierarchy between these agents, assigning their roles and the tools they can use.

• The LLM takes charge of the execution flow, deciding what actions to perform next.

• It's like an AI team working collaboratively, with an AI leading the way.

Level 5: Autonomous Pattern

At the highest current level, the LLM is basically an engineer. The AI model can operate with considerable independence and is capable of generating and executing new code on its own. It writes code, runs it, evaluates what happened, then writes more code—all without being told what to do next. It's not just following instructions; it's creating the playbook.

• The LLM can generate new pieces of code by itself.

• It can also execute this newly created code independently.

• The system effectively functions as if it were an independent AI developer.

• This pattern represents the most advanced form of AI agency currently observed.

• Direct human intervention during task execution is minimal.

Conclusion:

So, there you have it, climbing the ladder and moving from AI that simply answers questions to AI that can potentially build new things on its own. Agentic AI systems are getting better fast, and with them, so is the role of human input. Understanding these different levels of agency helps us see where this technology is today and gives us a clearer picture of how AI might help, collaborate, or even operate more independently in the future. These levels aren't just technical differences—they define how much trust, control, and oversight you need to give. After understanding these 5 agentic AI systems, you'll know what you're really building: a tool, a teammate, or an entirely new kind of worker.