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What are AI Agents?

Agents overview

Most AI models just answer questions. AI agents, on the other hand, think, act, observe, and improve.

Think of an AI agent like a smart assistant doing a task step by step

  1. Think – “What should I do first?”
  2. Act – Uses a tool, runs a search, writes code, etc.
  3. Observe – Checks what happened
  4. Reflect – “Did this work? What went wrong?”
  5. Try again – Improves the next step

This loop continues until the task is done.

📋 You’ll often see agents reason like this:

Thought: I need more information Action: Search the database Observation: Found relevant data Thought: Now I can answer

⚡This pattern is called ReAct (Reason + Act).

🪞 Reflection makes agents smarter Agents don’t just act—they review their mistakes. If something fails, the agent reflects on why and adjusts its plan, similar to how humans learn from errors.

🧰 Tools matter Agents become powerful because they can use tools:

🔍 Search the web or an internal knowledge base 🗄️ Query a database 📄 Read or write files 🧮 Run calculations or code 📡 Call APIs (GitHub, Slack, CRM, cloud services)

But more tools aren’t always better—too many tools can confuse the agent, just like humans.

💡 In simple terms: An AI agent is not just answering questions—it’s planning, using tools, learning from feedback, and adapting.

That’s what makes agents useful for real-world, multi-step tasks.