Getting Started with AI Agents

An AI agent is a system that uses a language model to decide what actions to take, executes those actions using tools, and observes the results to determine next steps.

Core Concepts

The agent loop: Perceive → Reason → Act → Observe → Repeat.

Tools: Functions the agent can call. Examples: web search, code execution, database queries, API calls.

Memory: How the agent retains context across steps. Short-term (within a conversation), long-term (vector stores or databases).

Framework Options

Framework	Language	Best For
Claude API (native)	Python / JS	Simple tool use, direct control
LangChain	Python / JS	Complex chains, many integrations
AutoGen	Python	Multi-agent conversations
CrewAI	Python	Role-based multi-agent teams

Quickstart with Claude

import anthropic

client = anthropic.Anthropic()

tools = [
    {
        "name": "get_weather",
        "description": "Get the current weather for a location",
        "input_schema": {
            "type": "object",
            "properties": {
                "location": {"type": "string"}
            },
            "required": ["location"]
        }
    }
]

response = client.messages.create(
    model="claude-opus-4-6",
    max_tokens=1024,
    tools=tools,
    messages=[{"role": "user", "content": "What's the weather in Paris?"}]
)

Next Steps

• Add more tools (web search, code execution)
• Implement a proper agent loop that processes tool results
• Add memory for multi-session context