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Capstone: Build Your Agent

Build your own sophisticated AI agent from scratch as a capstone project

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Building the Core Agent Logic

Your agent needs 4 core components working together: ReAct loop for reasoning, tool executor for actions, memory manager for context, and error handler for reliability.

Interactive: Core Component Explorer

Click each component to see implementation code and explanation:

🔄
ReAct Loop
class AgentCore:
    def __init__(self, llm, tools):
        self.llm = llm
        self.tools = {t.name: t for t in tools}
        self.memory = []
    
    def run(self, task, max_steps=10):
        for step in range(max_steps):
            # THINK: Reason about next action
            thought = self.llm.think(task, self.memory)
            
            # ACT: Choose and execute tool
            action = self.llm.choose_action(
                thought, self.tools.keys()
            )
            
            if action.name == "FINISH":
                return action.result
            
            # OBSERVE: Get tool result
            result = self.tools[action.name](
                action.input
            )
            
            # Update memory
            self.memory.append({
                'thought': thought,
                'action': action,
                'result': result
            })
💡How It Works
Core ReAct loop: Think → Act → Observe. Repeat until task complete or max steps reached.

Integration Pattern

These 4 components work together in your main agent class:

class ProductionAgent:
    def __init__(self, config):
        self.llm = LLM(config.model)
        self.tools = load_tools(config.tool_list)
        
        # Initialize all 4 core components
        self.react_loop = AgentCore(self.llm, self.tools)
        self.executor = ToolExecutor(self.tools)
        self.memory = MemoryManager(max_tokens=4000)
        self.error_handler = ErrorHandler(max_retries=3)
    
    def run(self, task):
        try:
            # Get relevant context from memory
            context = self.memory.get_context(task)
            
            # Run ReAct loop with context
            result = self.react_loop.run(
                task, 
                context=context,
                executor=self.executor
            )
            
            # Save to memory
            self.memory.add({
                'task': task,
                'result': result,
                'steps': self.react_loop.memory
            })
            
            return result
            
        except Exception as e:
            # Handle errors gracefully
            action = self.error_handler.handle(e, {
                'task': task,
                'retry_count': 0
            })
            
            if action == 'RETRY':
                return self.run(task)  # Recursive retry
            elif action == 'FALLBACK':
                return self.fallback_response(task)
            else:
                raise e

Best Practices for Production

  • •Logging: Log every tool call, error, and decision for debugging
  • •Timeouts: Set max execution time per tool (30s) and total task (5min)
  • •Cost Tracking: Count tokens per request, set budget limits
  • •Validation: Validate tool inputs before execution, outputs before returning
🚀
Next Steps

Once your core components are working:

  • 1.Test each component in isolation with unit tests
  • 2.Test integration with realistic scenarios
  • 3.Measure success rate, latency, cost per task
  • 4.Iterate based on failures and bottlenecks
← Project Setup