Welcome to the first module of our AI Agent Learning Path. By the end of this series, you’ll understand how to build production-ready AI agents for enterprise environments. Let’s start with the fundamentals.

What you’ll learn in this module

This module covers the essential foundations:

  • What an AI agent actually is (and isn’t)
  • The core components every agent needs
  • Your first working agent implementation
  • Common pitfalls and how to avoid them

What is an AI agent?

An AI agent is a system that can perceive its environment, make decisions, and take actions to achieve specific goals. Unlike traditional software that follows fixed rules, agents can:

Adapt their behavior based on context and feedback Make decisions using reasoning and learning capabilities Take actions through tool use and API integrations Maintain state across multiple interactions

The key distinction: agents exhibit autonomous behavior within defined boundaries.

Core components of an agent

Every functional agent has three essential components:

1. The Brain (LLM + Reasoning)

The language model provides:

  • Natural language understanding
  • Reasoning and planning capabilities
  • Decision-making logic
from anthropic import Anthropic

client = Anthropic(api_key="your_api_key")

def agent_brain(prompt, context):
    """The reasoning core of your agent"""
    response = client.messages.create(
        model="claude-3-5-sonnet-20241022",
        max_tokens=1024,
        messages=[{
            "role": "user",
            "content": f"Context: {context}\n\nTask: {prompt}"
        }]
    )
    return response.content[0].text

2. The Tools (Capabilities)

Tools let your agent interact with the world:

  • API calls
  • Database operations
  • File system access
  • External services
tools = {
    "search": lambda query: search_api(query),
    "calculate": lambda expression: eval(expression),
    "store_data": lambda key, value: database.set(key, value)
}

def execute_tool(tool_name, params):
    """Execute a tool action"""
    if tool_name in tools:
        return tools[tool_name](**params)
    raise ValueError(f"Unknown tool: {tool_name}")

3. The Memory (State Management)

Memory helps agents maintain context:

  • Conversation history
  • Task state
  • Learned information
  • User preferences
class AgentMemory:
    def __init__(self):
        self.conversation_history = []
        self.task_state = {}

    def add_message(self, role, content):
        self.conversation_history.append({
            "role": role,
            "content": content
        })

    def get_context(self):
        # Return relevant context for the current task
        return "\n".join([
            f"{msg['role']}: {msg['content']}"
            for msg in self.conversation_history[-5:]
        ])

Building your first agent

Let’s combine these components into a simple but functional agent:

class SimpleAgent:
    def __init__(self, api_key):
        self.client = Anthropic(api_key=api_key)
        self.memory = AgentMemory()
        self.tools = self._initialize_tools()

    def _initialize_tools(self):
        return {
            "get_weather": self.get_weather,
            "calculate": self.calculate,
        }

    def get_weather(self, location):
        # Simplified weather API call
        return f"The weather in {location} is sunny, 72°F"

    def calculate(self, expression):
        try:
            return str(eval(expression))
        except:
            return "Invalid calculation"

    def run(self, user_input):
        # Add user message to memory
        self.memory.add_message("user", user_input)

        # Get context from memory
        context = self.memory.get_context()

        # Prepare the prompt with available tools
        system_prompt = f"""You are a helpful agent with access to these tools:
        - get_weather(location): Get weather for a location
        - calculate(expression): Evaluate a mathematical expression

        Respond to the user's request. If you need to use a tool,
        respond with: TOOL: tool_name(arguments)"""

        # Get agent's response
        response = self.client.messages.create(
            model="claude-3-5-sonnet-20241022",
            max_tokens=1024,
            system=system_prompt,
            messages=[{
                "role": "user",
                "content": f"{context}\n\n{user_input}"
            }]
        )

        agent_response = response.content[0].text

        # Check if agent wants to use a tool
        if agent_response.startswith("TOOL:"):
            # Parse and execute tool call
            tool_call = agent_response.replace("TOOL:", "").strip()
            # ... tool execution logic ...

        # Add agent response to memory
        self.memory.add_message("assistant", agent_response)

        return agent_response

# Use the agent
agent = SimpleAgent(api_key="your_api_key")
response = agent.run("What's the weather in San Francisco?")
print(response)

Common pitfalls for beginners

1. No error handling Always handle tool failures gracefully. Agents will encounter errors—plan for them.

2. Unbounded memory Don’t store unlimited history. Implement memory pruning strategies.

3. No tool validation Validate tool inputs before execution. Never trust LLM output blindly.

4. Missing guardrails Set clear boundaries. Define what your agent can and cannot do.

What’s next?

You’ve built your first agent! In the next modules, we’ll cover:

Module 2: Adding structured tool use with function calling Module 3: Implementing the ReAct pattern for better reasoning Module 4: Memory strategies for production agents Module 5: Error handling and observability

Practice exercises

Before moving to Module 2, try these exercises:

  1. Add a new tool to your agent (suggestion: a simple database lookup)
  2. Implement conversation history limits (keep only last 10 messages)
  3. Add input validation to prevent malicious tool use
  4. Create a simple logging system to track agent decisions

Key takeaways

  • Agents combine LLMs, tools, and memory to accomplish tasks
  • Start simple: brain + tools + memory is all you need
  • Plan for failures: error handling is not optional
  • Set boundaries: agents need guardrails

Ready for more? Continue to Module 2 where we’ll implement proper tool calling with the Claude SDK’s function calling features.

This article is part of the “Building Production AI Agents” learning path. Follow the series for a complete understanding of agentic systems.