Building AI Agents: From Concept to Production

What Are AI Agents?

AI agents are autonomous systems that can perceive their environment, make decisions, and take actions to achieve specific goals. Unlike traditional chatbots, agents can:

• Break down complex tasks into subtasks
• Use tools and APIs
• Maintain context across interactions
• Learn and adapt from feedback

Core Components

1. The Language Model

The foundation of modern AI agents is a large language model (LLM) that provides:

• Natural language understanding
• Reasoning capabilities
• Decision-making

from anthropic import Anthropic

client = Anthropic()

def get_completion(prompt: str) -> str:
    response = client.messages.create(
        model="claude-3-5-sonnet-20241022",
        max_tokens=1024,
        messages=[{"role": "user", "content": prompt}]
    )
    return response.content[0].text

2. Tool Use

Agents need tools to interact with the world:

tools = [
    {
        "name": "search_web",
        "description": "Search the web for information",
        "input_schema": {
            "type": "object",
            "properties": {
                "query": {"type": "string"}
            },
            "required": ["query"]
        }
    },
    {
        "name": "read_file",
        "description": "Read contents of a file",
        "input_schema": {
            "type": "object",
            "properties": {
                "path": {"type": "string"}
            },
            "required": ["path"]
        }
    }
]

3. Memory Systems

Effective agents need both short-term and long-term memory:

• Short-term: Conversation context
• Long-term: Persistent knowledge storage

The Agent Loop

The core agent loop follows this pattern:

def agent_loop(task: str):
    messages = [{"role": "user", "content": task}]

    while True:
        response = client.messages.create(
            model="claude-3-5-sonnet-20241022",
            tools=tools,
            messages=messages
        )

        if response.stop_reason == "end_turn":
            return response.content[0].text

        # Process tool calls
        for block in response.content:
            if block.type == "tool_use":
                result = execute_tool(block.name, block.input)
                messages.append({
                    "role": "user",
                    "content": [{
                        "type": "tool_result",
                        "tool_use_id": block.id,
                        "content": result
                    }]
                })

Planning Strategies

ReAct Pattern

The ReAct (Reason + Act) pattern alternates between reasoning and action:

1. Thought: Analyze the situation
2. Action: Choose and execute a tool
3. Observation: Process the result
4. Repeat until task completion

Plan-and-Execute

For complex tasks, generate a plan first:

1. Break the task into steps
2. Execute each step
3. Revise the plan based on results

Error Handling

Robust agents need comprehensive error handling:

def safe_tool_execution(tool_name: str, inputs: dict):
    try:
        result = execute_tool(tool_name, inputs)
        return {"success": True, "result": result}
    except Exception as e:
        return {
            "success": False,
            "error": str(e),
            "suggestion": "Try a different approach"
        }

Best Practices

1. Be specific with tool descriptions - Clear descriptions help the model choose the right tool
2. Implement timeouts - Prevent infinite loops
3. Log everything - Essential for debugging
4. Use guardrails - Limit what the agent can do
5. Test extensively - Edge cases matter

Production Considerations

When deploying agents to production:

• Rate limiting: Respect API limits
• Cost monitoring: Track token usage
• Safety: Implement content filtering
• Observability: Use structured logging
• Fallbacks: Have human-in-the-loop options

Conclusion

Building AI agents requires careful consideration of architecture, tools, and safety measures. Start simple, iterate, and always prioritize reliability over complexity.

The future of AI is agentic - start building today!