What Is an AI Agent?

An AI agent is a system where a large language model (LLM) serves as the reasoning engine and can take autonomous actions — calling APIs, browsing the web, writing files, or executing code — to accomplish a goal. Unlike a simple chatbot, an agent plans, acts, observes the results, and iterates until the task is complete.

Core Concepts

Every AI agent follows the same basic loop: (1) Receive a goal, (2) Plan the next action, (3) Execute the action (call a tool), (4) Observe the result, (5) Repeat until done.

Step 2: Define Tools

{"name": "web_search", "description": "Search the web for current information", "input_schema": {"type": "object", "properties": {"query": {"type": "string"}}, "required": ["query"]}}

Step 3: The Agent Loop

import anthropic
client = anthropic.Anthropic()

def run_agent(goal):
    messages = [{"role": "user", "content": goal}]
    while True:
        response = client.messages.create(model="claude-sonnet-4-6", max_tokens=4096, tools=tools, messages=messages)
        if response.stop_reason == "end_turn":
            return response.content[0].text
        for block in response.content:
            if block.type == "tool_use":
                result = execute_tool(block.name, block.input)
                messages.append({"role": "assistant", "content": response.content})
                messages.append({"role": "user", "content": [{"type": "tool_result", "tool_use_id": block.id, "content": result}]})

Step 5: Agent Frameworks

  • Anthropic Agent SDK — Best for Claude-based agents
  • LangChain/LangGraph — Largest ecosystem, good for multi-agent workflows
  • LlamaIndex — Best for document-heavy RAG agents
  • CrewAI — Best for multi-agent role-based systems

Conclusion

Building AI agents in 2026 is more accessible than ever. Start with a single tool and a simple goal, master the agent loop, then progressively add memory and multi-agent coordination.