What is an AI Agent? A Simple Guide to Understanding the Technology

Imagine an AI that doesn't just respond to your questions, but actually thinks through problems step by step, uses tools when needed, and explains its reasoning along the way. That's exactly what ReAct (Reasoning + Acting) agents do.

AI agents are everywhere—promising to skyrocket your productivity, transform entire industries, and even let a single person build the next unicorn startup. 🦄

But with all the hype, it's easy to feel lost. What exactly is an agent? How are they different from LLMs? And do they really live up to the promise of revolutionizing work?

But sometimes you need to go back to first principles to have more control, more robustness and cheaper solutions in return of sacrificing the scope by reducing it drastically. That's when understanding the fundamentals becomes crucial.

Yet the mechanics are surprisingly simple—so simple that once you see it, you'll wonder why it seemed so mysterious.

But here's the key insight: agents are neither more nor less than orchestrated LLMs—they're LLMs with a coordination layer that manages tools, context, and workflows.

Spending time talking with an LLM, asking it questions, and copy-pasting context from other sources? What if you could automate all of that? And what if I told you, you could go even further—with an autonomous LLM that not only answers but acts for you? That's what we call an agent.

🏗️ The Agent Stack: How It All Works Together

🚀 Simple Example: Restaurant Booking

You: "Find me a good restaurant for tonight"

At their core, agents loop through sensing the world, reasoning about what to do next, acting via tools, and updating state. This is just software orchestration around an LLM.

# Minimal ReAct loop (pseudocode)
state = {"goal": user_query, "history": []}
for step in range(max_steps):
    thought = llm.think(state)                 # decide next best step
    action = parse_action(thought)             # e.g., search["..."] or read_url["..."]
    observation = tools.run(action)            # execute tool and get result
    state = update(state, thought, action, observation)
    if done(state):
        break
answer = llm.final_answer(state)

⚡ The ReAct Pattern: The Reactive Generalist

ReAct Pattern Components

{
  "step": "waiting",
  "progress": "0/5",
  "content": null
}

🚀 Real Example: Planning a Weekend Trip

This back-and-forth between thinking and acting is what makes ReAct agents powerful. But they're not perfect—they can get stuck in loops or be derailed by bad tool outputs.

🚀 Beyond ReAct: The Evolution of Agent Architectures

While ReAct is foundational, the most sophisticated agents today use composed architectures that combine multiple patterns:

Here are some concrete examples where different agent patterns shine:

Before you get too excited, let's be clear about what each pattern can and can't do:

While agent patterns are conceptually simple, implementing robust production systems comes with real challenges:

🔧 Core Technical Challenges

🎭 The Composition Challenge

The most powerful agents combine multiple patterns, but this introduces new complexities:

Multi-Agent System
├── Project Manager (Plan-and-Solve pattern)
├── Architect (ReAct for research + Reflection for design quality)  
├── Programmer (ReAct for coding + Reflection for code quality)
└── QA Tester (Reflection pattern for thorough testing)

📊 Observability and Monitoring: Why It Matters More Than Ever

Building robust agents isn't just about reasoning and tool use—it's about knowing what your agent is doing, when, and why. With complex multi-pattern agents, observability becomes critical.

🛡️ Ethical and Safety Considerations

🚀 Emerging Solutions

The good news? The ecosystem is rapidly evolving with new standards and frameworks:

This combination has indeed made ReAct the most widely adopted pattern, and for good reason. It's the foundation that most successful agents build upon. However, the most successful agents today don't just use ReAct—they strategically compose multiple patterns.

🚀 The Evolution of Success

🎯 Choosing the Right Pattern

The future may not be about finding the "one true pattern"—it's about mastering the art of composing these building blocks to create intelligent systems tailored to real-world needs.

✅ Practical Implementation Checklist