Agentic systems are the new black of AI. They're making interactions with artificial intelligence faster, more user-friendly, and refreshingly seamless. But how does an agentic system work? What makes it different from prompt-based approaches? Do you even need one in your workflow? (pro tip: you do).
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You'll find answers to these and other questions in this article. Let's dive in!
🤖 What Are Agentic AI Systems?
Before we get into the nitty-gritty, we should precede this question with another: "What are AI agents?" If you haven't read our previous articles on the topic (highly recommended), here's a tl;dr.
In a purely technical sense, AI agents are autonomous software entities designed to perceive, reason about, collaborate, and act within their environment to achieve specific goals.
Ok, but what does that mean exactly?
Take, for example, Tesla's Autopilot. It uses sensors and cameras to see the road, processes this info to understand what's happening, and then drives the car by steering, braking, and accelerating. Each individual system making up the Autopilot acts as an agent---one agent handles navigation, another monitors obstacles, and yet another ensures lane discipline.
All this happens without or with minimal human intervention --- the hallmark of agentic design.
With that in mind, grasping the essence of agentic system should be a tad easier.
An agentic system is an integrated environment of agents, tools, and data sources, with a large language model (LLM) like GPT-4o in the center, acting as the decision-making engine.
An example of an agentic system imitating a marketing team.
According to AI researcher Andrew Ng, there are four characteristics of agentic systems:
- 💭 The ability to reflect on generated output to identify improvements.
- ⚒️ The ability to use tools such as web searches, code execution, or other functions.
- ✅ The ability to plan & execute tasks in multiple steps.
- 🤝 The ability to collaborate collaborate with other agents.
If you're thinking: "hey, that's just like us," you're right. By design, agentic systems mimic the dynamics of human teams --- agents work together, leverage each other's strengths, and adapt to achieve goals.
So, what makes agentic systems better than other AI approaches?
⚡ Benefits of Agentic AI in the Workplace
Typical AI interactions rely on prompting. You give a chat-based tool like ChatGPT a prompt, such as "draft an email reply," and you get a response. Then, you usually need to refine the tone through multiple iterations and send the email. That's a lot of manual, time-consuming work.
With agentic systems, all you need to do is define a goal. The agents in the system then break it down into steps, continuously reprompting themselves until the goal has been completed.
For example, for a goal: "Create and execute a marketing plan," the agents may coordinate like this:
| 🤖 (Agent 1) | Researches market trends and target demographics. |
| 🤖 (Agent 2) | Develops a timeline for the launch. |
| 🤖 (Agent 3) | Drafts promotional content and strategies. |
| 🤖 (Agent 4) | Schedule the publication of marketing materials. |
This approach is more flexible and robust than prompt-based interactions. It gives AI the autonomy to ideate, plan, and execute based on the available context, with no or minimal human intervention.
Agentic workflows make interactions with AI simpler, faster, and more intuitive, exactly what they're supposed to be. But that's barely scratching the surface, so let's talk about a few more benefits.
Step-by-Step Problem Solving
Find out more about agentic AI systems on the official Taskade blog.
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