Python
# Basic Agent Pattern
class Agent:
    def __init__(self):
        self.memory = VectorStore()
        self.tools = ToolRegistry()
        self.planner = ReActPlanner()
    
    def execute(self, task):
        plan = self.planner.create_plan(task)
        return self.planner.execute_with_tools(plan, self.tools)

Progress:

• Choose base pattern (ReAct, Chain-of-Thought, or Multi-Agent)
• Define tool interface and capabilities
• Implement memory/context management
• Add feedback and error handling
• Test with representative tasks

1. Select Architecture Pattern

ReAct Pattern: Reasoning + Acting cycles for tool use Chain-of-Thought: Step-by-step reasoning with external verification
Multi-Agent: Specialized agents with orchestration layer

2. Define Core Capabilities

• Tool Integration: Function calling, API wrappers, validation
• Memory Management: Context windows, vector storage, retrieval
• Planning Logic: Task decomposition, dependency tracking, execution order

3. Implementation Framework

• Use LangChain/AutoGen for orchestration
• Implement function calling for tool access
• Add structured output parsing
• Build feedback loops for improvement

Example 1: Research Agent Input: "Analyze market trends for electric vehicles"

Python
tools = [WebSearchTool(), DataAnalysisTool(), ReportGenerator()]
agent = ReActAgent(tools=tools)
# Output: Multi-step research with sources, analysis, and structured report

Example 2: Code Assistant Input: "Debug this Python function and suggest improvements"

Python
tools = [CodeAnalyzer(), TestRunner(), DocumentationTool()]
agent = ChainOfThoughtAgent(tools=tools)
# Output: Error identification, fix suggestions, test cases, docs

Example 3: Multi-Modal Analyst Input: "Analyze this document and create presentation slides"

Python
specialists = [PDFAgent(), DataVizAgent(), SlideAgent()]
orchestrator = MultiAgentSystem(specialists)
# Output: Coordinated analysis across document types with final presentation