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Sol / Luna Recursive Task Decomposition Strategy - hono-docs

Sol / Luna Recursive Task Decomposition Strategy Source: /Sources/chatgpt/agent戦略作成 28e9980f.md Generated by: wiki generator v1 Summary This page outlines a...

Sol / Luna Recursive Task Decomposition Strategy

Summary

This page outlines a recursive task decomposition strategy for an AI agent system, where an orchestrator (Sol) delegates tasks to worker agents (Luna Worker). When a worker gets stuck, Sol stops it, analyzes the blocker, splits the task into two smaller subtasks, and delegates them to new workers. This binary tree approach continues recursively until tasks are solved or a depth limit is reached, emphasizing failure-aware decomposition and avoiding blind retries.

Key Facts

  • Sol acts as the orchestrator/planner/supervisor, while Luna Worker is the atomic task executor. (source)
  • When a Luna Worker gets stuck, Sol stops the worker and decomposes the task into two smaller subtasks, delegating them to new workers. (source)
  • The strategy forms a binary tree of tasks, with each stuck task being split into two children. (source)
  • Workers must be given clear task specifications including Goal, Context, Inputs, Constraints, Expected Output, Success Criteria, and Stop Conditions. (source)
  • STUCK is distinguished from FAILED: STUCK means the task can potentially be solved by decomposition, while FAILED indicates the task itself is not viable. (source)
  • A depth limit (e.g., MAX_DEPTH = 5) prevents infinite recursion, after which Sol re-evaluates the task rather than spawning more workers. (source)
  • Atomic tasks should not be split further; instead, change approach, escalate, request missing dependency, or reconsider assumptions. (source)
  • Workers should return structured results with status: SUCCESS, STUCK, or FAILED, including relevant details. (source)

Decisions

  • Adopt the Sol/Luna recursive task decomposition strategy as described in the agent.md document. (source)
  • Stop workers when they are stuck and do not let them retry the same task blindly. (source)
  • Use binary decomposition as the default, but only when it yields independent, simpler, testable subtasks. (source)
  • Incorporate worker failure reports into the next decomposition step (failure-aware decomposition). (source)
  • Terminate stuck workers and spawn new ones for subtasks, preserving only the failure report. (source)

Open Questions

  • What specific MAX_DEPTH value will be used in practice? (source)
  • How will Sol validate worker results beyond Success Criteria checks? (source)
  • How will the integration of results from subtasks handle conflicting outputs? (source)

Follow-Ups

  • Implement the Sol/Luna strategy in an agent system and test with a prototype task. (source)
  • Define concrete policies for when to escalate vs. re-plan at depth limit. (source)
  • Develop integration tests to validate the merge logic for successful subtasks. (source)

Related Context

Provenance

  • source_path: /Sources/chatgpt/agent戦略作成-28e9980f.md
  • source_etag: v4h:d04c145db769211535b733f67d590b96cb3c83dc0f34db41c41ebb8a4662b7cb
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