agents

Building Agents

Agents are systems where LLMs dynamically direct their own processes and tool usage. This skill covers when to use agents vs workflows, common architectural patterns, and practical implementation guidance.

Table of Contents

• Agents vs Workflows
• Workflow Patterns
• Agent Architectures
• ReAct Pattern
• Tool Design
• External Context Protocols
• Best Practices
• References

Agents vs Workflows

Aspect	Workflows	Agents
Control flow	Predefined code paths	LLM determines next step
Predictability	High - deterministic steps	Lower - dynamic decisions
Complexity	Simpler to debug and test	More complex, harder to predict
Best for	Well-defined, repeatable tasks	Open-ended, adaptive problems

Key principle: Start with the simplest solution. Use workflows when the task is predictable; use agents when flexibility is required.

Workflow Patterns

1. Prompt Chaining

Decompose tasks into sequential LLM calls, where each step's output feeds the next.

async def prompt_chain(input_text):
    # Step 1: Extract key information
    extracted = await llm.generate(
        "Extract the main entities and relationships from: " + input_text
    )

    # Step 2: Analyze
    analysis = await llm.generate(
        "Analyze these entities for patterns: " + extracted
    )

    # Step 3: Generate output
    return await llm.generate(
        "Based on this analysis, provide recommendations: " + analysis
    )

Use when: Tasks naturally decompose into fixed sequential steps.

2. Routing

Classify inputs and direct them to specialized handlers.

async def route_request(user_input):
    # Classify the input
    category = await llm.generate(
        f"Classify this request into one of: [billing, technical, general]\n{user_input}"
    )

    handlers = {
        "billing": handle_billing,
        "technical": handle_technical,
        "general": handle_general,
    }

    return await handlers[category.strip()](user_input)