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Subtitle:

Understanding the technical difference between agent architecture and workflow architecture in LLM applications

Core Idea:

In LLM application design, agents operate through "tool calling in a loop" where they freely make decisions about which tools to use until completion, while workflows follow predefined control flow paths that may incorporate LLM calls at specific points.

Key Principles:

1. Execution Pattern:
   • Agents: Tool calling continues in a loop until no further tool calls are made
   • Workflows: Follow a predetermined control flow with specific decision points
2. Decision Authority:
   • Agents: The LLM decides which tools to call and when to terminate
   • Workflows: The developer pre-defines the possible paths and conditions
3. Tool Integration:
   • Agents: Can freely choose from available tools without predefined sequence
   • Workflows: Tools and LLM calls are integrated at specific predefined points

Why It Matters:

• Implementation Complexity:
  • Workflows often require more upfront development but offer greater control
  • Agents require less code but may have less predictable behavior
• Model Requirements:
  • Agents require models with strong function/tool calling capabilities
  • Workflows can use simpler models for specific tasks within the flow
• Performance With Small Models:
  • Local and smaller models often perform better in workflow architectures
  • Tool calling in a loop (agents) requires more sophisticated model capabilities

How to Implement:

1. Agent Implementation:
   • Define tools and their interfaces
   • Implement a tool-calling loop that continues until completion
   • Use models with strong function calling abilities (e.g., Qwen series for local deployment)
2. Workflow Implementation:
   • Map out the entire process flow with decision points
   • Integrate LLM calls at specific steps where needed
   • Use structured outputs for routing between steps

Example:

• Scenario:
  • Creating a research assistant application
• Application:
  • Agent Approach: LLM decides which search tools to use, when to summarize, and when task is complete
  • Workflow Approach: Developer creates explicit flow: query classification → tool selection → execution → formatting
• Result:
  • Agent provides more flexibility but requires stronger models
  • Workflow offers more reliability and works better with smaller models

Connections:

• Related Concepts:
  • AI Agents vs Automations: Broader comparison of autonomous vs rule-based systems
  • AI Agent vs. Workflow Automation: Implementation considerations for both approaches
• Broader Concepts:
  • Local LLM Agents: Implementation of agents on local hardware
  • Multi-Agent Systems: Extension of agent architecture to multiple specialized agents

References:

1. Primary Source:
   • LangChain documentation on agents vs workflows
2. Additional Resources:
   • Berkeley Function Calling Leaderboard for evaluating model capabilities
   • LangGraph documentation for implementing both patterns

Tags:

#agent-architecture #workflow-architecture #langchain #tool-calling #control-flow #implementation-patterns

Connections:

Sources:

• From: LangChain - Fully local multi-agent systems with LangGraph