Building Effective Agents

A Guide to LLM Implementation Patterns

December 19, 2024

Key Insights

  • Simple, composable patterns outperform complex frameworks
  • Success comes from appropriate implementation choices
  • Focus on measurable outcomes over complexity

Understanding Agents

Workflows

Systems where LLMs and tools follow predefined code paths

Agents

Systems where LLMs dynamically direct their own processes and tool usage

When to Use Agents?

  • Start with the simplest solution possible
  • Consider the tradeoff between latency/cost and task performance
  • Use workflows for predictability in well-defined tasks
  • Choose agents when flexibility and model-driven decisions are needed

Core Building Block: Augmented LLM

  • Enhanced with retrieval capabilities
  • Integrated with tools
  • Memory management
  • Self-directed search and tool selection

Key Workflow Patterns

  1. Prompt Chaining
  2. Routing
  3. Parallelization
  4. Orchestrator-Workers
  5. Evaluator-Optimizer
Best for: Tasks with clear, fixed subtasks

Routing Pattern

  • Classifies input for specialized handling
  • Enables optimization for specific cases
  • Separates concerns effectively

Best for: Complex tasks with distinct categories

Parallelization

Sectioning

Breaking tasks into parallel subtasks

Voting

Running multiple instances for consensus

Orchestrator-Workers Pattern

  • Central LLM coordinates task breakdown
  • Dynamically delegates to workers
  • Synthesizes results

Best for: Complex, unpredictable task structures

Evaluator-Optimizer Pattern

  • One LLM generates responses
  • Another provides feedback
  • Iterative improvement loop

Best for: Tasks with clear evaluation criteria

Autonomous Agents

  • Independent operation after initial command
  • Environmental feedback integration
  • Checkpoint-based human oversight
  • Clear success criteria

Success Stories

Customer Support

  • Natural conversation flow
  • Integrated tool access
  • Measurable outcomes

Coding Agents

  • Verifiable solutions
  • Structured problems
  • Automated testing

Implementation Principles

  1. Maintain simplicity in design
  2. Prioritize transparency
  3. Carefully craft agent-computer interfaces