Design & Implement Plan-and-Execute Agent Architecture

[ashen-dusk]

🎯 Goal

Refactor or extend the existing LangGraph agent to adopt a “Plan → Execute → Re-Plan” architecture:

• First generate a multi-step plan.
• Then execute each step via tools/agents.
• Optionally re-plan if results necessitate it.
  Based on the LangGraph “Plan-and-Execute” tutorial. ([langchain-ai.github.io][1])

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✅ Requirements

• . Planning Phase

  • Define the planning node: use an LLM to output a list of steps (structured).
  • Represent state: input, plan (array of steps), past steps, response. ([langchain-ai.github.io][1])
  • Use a prompt (or tool) for planning: e.g., system message asking for step-by-step plan. ([langchain-ai.github.io][1])

• . Execution Phase

  • Define execution node(s): each plan step is executed via agents/tools.
  • Maintain past_steps list of tuples: (step, result). ([langchain-ai.github.io][1])

• . Re-Planning Phase

  • After executing a step (or set of steps), check if plan is complete or needs revision.
  • If more work is needed, invoke replanner to update plan. ([langchain-ai.github.io][1])

• . Graph / Workflow Structure

  • Use a directed graph: nodes = planner, executor, replanner.
  • Edges: START → planner → executor → replanner → either executor or END based on condition. ([langchain-ai.github.io][1])
  • Compile graph into runnable agent (via LangGraph).

• . Integration with existing agent

  • Map existing tools and agent logic into this architecture.
  • Ensure backward compatibility with current flows (if any).
  • Ensure state management (input, plan, steps, response) aligns with existing state objects.