Building Your First AI Agent: A Practical Guide with Claude Agent SDK

📁 Context Gathering Tools

# File system search capabilities
def search_documents(query, file_types=['.pdf', '.txt', '.md']):
    """Search through documents for relevant information"""
    # Use bash commands to search file contents
    # grep -r "query" ./documents --include="*.pdf" --include="*.txt"
    pass

def get_document_summary(file_path):
    """Extract key information from a document"""
    # Read file, extract metadata, identify key topics
    pass

def understand_user_query(query):
    """Parse and understand what the user is asking for"""
    # Break down the request into actionable tasks
    pass

⚡ Action Tools

# Core action capabilities
def generate_research_summary(topic, sources):
    """Create a comprehensive summary from multiple sources"""
    # Analyze sources, extract key points, synthesize information
    pass

def answer_question(question, context_documents):
    """Answer specific questions based on available documents"""
    # Search relevant sections, provide evidence-based answers
    pass

def create_research_report(topic, findings, format='markdown'):
    """Generate a formatted research report"""
    # Structure findings, add citations, format output
    pass

def update_knowledge_base(new_information):
    """Add new information to the research database"""
    # Process and categorize new information for future use
    pass

✅ Verification Tools

# Quality assurance capabilities
def verify_summary_completeness(summary, sources):
    """Check if summary covers all important points from sources"""
    # Compare summary against source material
    pass

def check_fact_accuracy(claims, source_documents):
    """Verify that claims are supported by source material"""
    # Cross-reference claims with original sources
    pass

def validate_report_structure(report):
    """Ensure report follows proper format and structure"""
    # Check formatting, completeness, logical flow
    pass

def test_answer_quality(question, answer, sources):
    """Evaluate if answer adequately addresses the question"""
    # Assess relevance, completeness, and accuracy
    pass

🔧 Advanced Features

• • Subagents: Spin up specialized agents for different tasks (search, analysis, formatting)
• • MCP Integration: Connect to external services like Slack, GitHub, or Google Drive
• • Visual Feedback: Use screenshots and visual verification for UI-related tasks
• • Code Generation: Let your agent write and execute code for complex operations
• • Parallel Processing: Run multiple operations simultaneously for better performance

💡 Essential Principles

• • Give your agent a computer: Access to file systems, command line, and code generation
• • Follow the three-phase loop: Gather context → Take action → Verify work
• • Design tools as primary actions: Make them prominent in the agent's context
• • Implement verification: Rules-based, visual, or LLM-as-judge feedback
• • Start simple, iterate: Build basic functionality first, then enhance
• • Test with real scenarios: Use representative examples to validate your agent