AI agents are quickly becoming the next big leap in artificial intelligence—far beyond basic chatbots or rule-based workflows. But what exactly is an AI agent? In simple terms, an AI agent is a goal-driven, reasoning-capable software entity that can make decisions, use tools, and adapt over time without constant human prompting. Unlike traditional bots or scripts, AI agents don’t just respond—they act.
In this blog, we’ll break down the evolution from LLMs to workflows to true AI agents, clarify what makes an agent “intelligent,” and explain how concepts like ReAct and RAG fit into the picture. Whether you’re building AI systems or just exploring the future of automation, this is your foundational primer on how AI agents are redefining productivity, autonomy, and decision-making.
What Are AI Agents?
AI agents are essentially autonomous AI-driven software entities that use reasoning and memory to decide on actions in pursuit of a goal. In simple terms, “AI agents are artificial intelligence that use tools to accomplish goals” (bcg.com). They can remember context across tasks, utilize one or more AI models, and decide when to call internal or external systems on a user’s behalf.
AI agents might manifest as digital assistants, process-automation bots, or “virtual employees.” They can take on goals like “generate a monthly marketing report and update the dashboard” or “troubleshoot this IT ticket end-to-end.” Unlike a standard script that follows a fixed procedure, an AI agent can adapt its approach on the fly. It observes the environment (data, user input, system state), uses AI reasoning to plan a solution, executes actions (querying databases, calling APIs, composing content, etc.), and continuously adjusts until the goal is achieved
Chatbots vs Workflows vs Agents
What’s the difference between a chatbot and an AI agent? What do terms like RAG or ReAct actually mean?
Let’s break it down using a simple, three-level framework.
Level 1: Large Language Models (LLMs)
Most people first interact with AI through chatbots like ChatGPT, Claude, or Google Gemini. These are powered by Large Language Models (LLMs)—AI systems trained on massive datasets to generate and edit text.
Think of it like this:
- You give an input (e.g., “Write an email to schedule a meeting with my team.”)
- The AI gives an output (a polite, well-written email proposal for a meeting).
These tools are great for content generation, idea exploration, or casual Q&A. But there’s a catch.
Say you ask: “When is my next team meeting?”
The LLM will fail. Why? Because:
- It doesn’t have access to your private data like your calendar.
- It’s passive—it only responds when prompted. It doesn’t take initiative or proactively look things up.
LLMs are powerful—but they’re limited by their lack of memory, context, and connectivity.
Level 2: AI Workflows
Now let’s build on the LLM with a workflow—a sequence of tasks that the AI can follow to complete a process.
For example:
You tell the AI: “Every time I ask about a meeting, check my Google Calendar first.”
Now when you ask: “When is my next team meeting?”
The LLM fetches the event from your calendar and responds correctly.
But what happens if you follow up with: “Can you book a room for it?”
It fails again—because you only told it to check your calendar, not reserve a room.
Here’s the key trait of AI workflows:
They follow predefined, human-designed paths (also called control logic).
The AI doesn’t decide the steps—it just executes them.
Let’s say you expand the workflow:
- Check your calendar for the meeting
- Reserve a room using a booking API
- Email attendees with the updated location
Still, this is just a workflow, because you’re still the one designing the logic. The AI is just a helper—not a decision-maker.
Bonus: What’s RAG?
Retrieval-Augmented Generation (RAG) is a term you might hear often. All it means is: the AI looks something up (like from your calendar or a database) before responding. It’s still part of a workflow, not an agent.
Level 3: AI Agents
Now comes the leap: turning workflows into AI agents.
To create an AI agent, you need to replace the human decision-maker with the AI itself.
An AI agent must be able to:
- Reason: Decide how to achieve the goal
- Act: Use tools or APIs to perform steps
- Iterate: Improve based on results, without human prompting
Let’s say the goal is: “Generate a LinkedIn post based on trending news.”
A workflow might look like:
- Scrape news articles
- Summarize them
- Generate a post
- Manually tweak if it’s not good enough
But an AI agent would:
- Decide the best way to find and summarize news
- Choose which tools to use (Google Sheets, APIs, etc.)
- Generate a LinkedIn post
- Critique the post using another AI model
- Improve and repeat until the post meets quality standards
Whether you’re building enterprise-grade automations or exploring what’s next in generative AI, understanding the foundations of AI agents is key to staying ahead. With frameworks like ReAct and architectures like A2A and MCP emerging fast, now is the time to start experimenting, designing, and deploying your own intelligent agents.
Ready to power AI agents with real-time, unified data?
Fragmented systems are the biggest blocker to scalable AI. Knowi provides the enterprise-grade foundation agents need—a unified layer connecting structured, unstructured, and API-driven sources.
Book a personalized demo to see how you can bring all your data together in one place.
Frequently Asked Questions
What is an AI agent?
An AI agent is an autonomous software system powered by artificial intelligence that can make decisions, use tools, and complete tasks in pursuit of a goal—without needing constant human prompts.
How is an AI agent different from a chatbot?
Chatbots respond to inputs using pre-trained language models, but they don’t take initiative or make decisions. AI agents, on the other hand, can reason, use memory, interact with systems, and adjust their actions dynamically.
What’s the difference between AI agents and AI workflows?
AI workflows follow predefined, human-created logic. AI agents go a step further by deciding how to complete tasks, choosing tools, and iterating based on feedback without any human intervention—making them far more flexible and autonomous.
What is ReAct in the context of AI agents?
ReAct stands for Reason + Act. It’s a framework that allows AI agents to plan actions based on reasoning and then execute them, enabling multi-step decision-making.
Can AI agents use tools like APIs or spreadsheets?
Yes, AI agents can autonomously interact with tools like APIs, databases, or spreadsheets —deciding when and how to use them to accomplish tasks.
