Adapted from1
There’s been a lot of buzz around the terms “AI agents” and “Agentic AI systems” lately. While they’re often used interchangeably, they actually refer to slightly different concepts.
- “Agentic AI systems” is more of a broad category, similar to “Generative AI”. It refers to AI systems that can take initiative, plan, and act autonomously.
- Meanwhile, “AI agents” are the actual systems or tools that do this agentic work.
So when people talk about agentic workflows, they are usually describing setups that involve one or more AI agents performing multi-step tasks with some level of independence.
A recent study lays out a clear taxonomy that helped me better understand this space.2 Based on that and our experience with some of today’s popular AI agents, I’ll highlight the key differences between AI agents and Agentic AI systems, including how they are architected, where they are useful.
What are AI agents?
AI agents are software systems built to perform specific tasks. When given a prompt or objective, they can call external tools, interact with APIs, or run a series of predefined steps to achieve a result.
AI agents are often built using frameworks like LangChain or OpenAI’s function-calling. There are also pre-built agents, like Project Mariner, designed for everyday tasks.
These tools typically operate within a specific scope, handling tasks such as summarizing text, sending emails, or retrieving calendar events.
What are agentic AI systems?
Source: Rajdev, Swapan3
Agentic AI systems refer to systems that go beyond single-task execution.
It is built to pursue high-level goals by breaking them into sub-tasks, sequencing those tasks, deciding what needs to happen next, and coordinating execution.
A common pattern you will see is: Perceive → Reason → Act → Learn
While AI agents typically handle a single step in a workflow, agentic AI can plan and execute the entire workflow, adapt to changes, and even coordinate with other agents. It’s a system of agents, not just one operating toward some higher-level objective.
What does it mean to be agentic?
Levels of agentic autonomy in AI systems
Level | Category | Capability | Status |
|---|---|---|---|
1–2 | ❌ Generative AI | Basic LLM text generation | ✅ Fully achieved |
3 | 🟩 AI Agents | Tool execution | ✅ Widely available |
4 | 🟩 Enhanced AI Agents | Contextual logic + short memory | ⚠️ Early-stage |
5 | 🟦 Agentic AI Systems | Reflective control + episodic memory | ⚠️ Experimental |
6 | 🟦 Future Agentic AI Systems | Fully orchestrated multi-agent coordination | ❌ Not yet achieved at scale |
These definitions can get technical, but they’re useful for thinking through how much autonomy or agent-like behavior an LLM-based system actually demonstrates.
A system becomes more agentic as the LLM gains more control over how it behaves.
For example, at the most basic generative AI level, you might use an LLM solely to generate text responses based on prompts. It processes input and returns output with no tools, no memory, and no ability to influence its behavior beyond that one interaction. This is a purely reactive setup.
When you incorporate tool execution, such as an LLM deciding to trigger a calculator, summarizer, or search function, the system reaches the AI agent level.
As more decision logic is layered in, often across multiple LLMs or modules, the system gains the ability to respond based on past interactions or input context. This is an enhanced AI agent. The system can maintain short-term memory, conditionally select tools, and follow slightly more dynamic task flows. Architecturally, this is similar to a context-aware state machine.
If the system can reflect on outcomes, determine whether to continue or halt, and leverage different types of memory (such as episodic or long-term), it fits within the agentic AI systems category. These systems manage their own task loops, make internal decisions, and adjust plans mid-process. This is where reflective control and planning begin to emerge.
At the highest level is future agentic AI systems, where systems feature fully orchestrated multi-agent coordination. These systems dynamically assign tasks, create or reuse tools, manage shared memory, and operate with long-horizon autonomy. They are designed not only to solve problems, but to organize how those problems are solved across agents, resources, and time.
Let’s break down to see what a typical agentic AI architecture looks like:
Behavioral comparison
Aspect of behavior | AI Agents | Agentic AI Systems |
|---|---|---|
Trigger mechanism | Activated by a user prompt or tool call | Activated by high-level goals or system context |
Goal handling | Executes fixed, predefined goals | Dynamically adapts, decomposes |
Core responsibility | Completes one or more task at a time | Manages entire workflows with multiple steps |
Task duration | Operates within a short session window | Maintains context across longer timelines |
Use of memory | Uses limited, temporary memory per task | Stores persistent memory across tasks |
Learning behavior | May adapt tool usage in narrow scope | Learns from outcomes to refine future actions |
Collaboration model | Functions independently | Coordinates with other agents or subsystems |
System design comparison
System element | AI Agents | Agentic AI Systems |
|---|---|---|
System setup | Model + tools | Team of agents with shared tasks |
Execution style | One-off tool execution | Continuous, goal-based orchestration |
How it gets data | Direct API or tool calls | Agent-level access across tools |
Interaction and processing flow
Component | AI Agents | Agentic AI Systems |
|---|---|---|
Interaction flow | User → Agent → Tool | User → System → Agents |
Data flow | Input → Tool → Output | Input → Agent 1 → Agent 2 → … → Output |
Adapted from Sapkota et al. (2025)efn_note]https://arxiv.org/pdf/2505.10468[/efn_note]
Applications of AI agents & agentic AI systems
AI agents applications (Task-oriented)
To understand where AI agents are most useful, let’s look at a few practical scenarios where these systems excel at handling narrowly scoped tasks:
- Data extraction from documents: These agents are built to pull structured information like names, totals, or dates from PDFs, forms.
- Basic scheduling assistants: Instead of manually finding time slots, these agents can check calendar availability, send invites, and handle rescheduling.
- Internal knowledge search: Given a question, these agents search internal wikis or databases to retrieve specific information.
- Email triage and prioritization: These agents classify emails, flag important ones, and route messages to the right team or system.
Agentic AI applications (Goal-oriented)
To understand how Agentic AI systems operate, let’s walk through a few real-world examples:
- Adaptive workflow automation: Instead of following a rigid task list, these systems can modify multi-step workflows based on what’s happening like adjusting a process when new data arrives or if an earlier step fails.
- Collaborative decision support: In complex areas like healthcare or legal analysis, multiple agents can process different pieces of information, reason through options, and provide coordinated recommendations similar to a digital team offering expert advice.
- Multi-agent simulations in games: Think of simulations where AI characters interact not with pre-scripted moves, but through autonomous decisions and real-time coordination, creating emergent, lifelike behavior.
- End-to-end scientific literature review: These systems can search academic databases, filter relevant papers, summarize findings, and even suggest conclusions, handling the entire research pipeline with minimal input.
Real-world Agentic AI demo: In a GitHub project, 10 AI agents collaborated to write a ~300-page novel by working as a coordinated system, with no human writing. Here is a livestream showing how the agents built the novel:
Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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