utility

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Score candidate agent actions by expected gain, cost, uncertainty, and redundancy to guide dispatch and termination decisions.

Code & Development 308 stars 27 forks Updated today MIT

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Skill Content

# Utility Skill ## Overview A decision framework for agent orchestration based on Liu et al., "Utility-Guided Agent Orchestration for Efficient LLM Tool Use" (arXiv:2603.19896). Each candidate action is scored by subtracting weighted costs from expected gain, producing a single utility value that guides action selection. The framework prevents over-calling tools and premature stopping by making both errors costly. Utility range is [-2.3, 1.0]. ## When To Use - Deciding whether to dispatch another agent or tool call - Gating expensive tool calls (search, code execution, delegation) - Selecting the right model tier for a sub-task - Continuation decisions after receiving partial results - Verification gating before writing or committing output ## When NOT to Use - Single-step operations with one obvious action - Trivial tasks where cost of scoring exceeds benefit - Already-committed actions that cannot be undone ## Action Space `A = {respond, retrieve, tool_call, verify, delegate, stop}` | Action | Description | |-----------|------------------------------------------------------| | respond | Emit a final answer from current context | | retrieve | Fetch additional information (search, read, lookup) | | tool_call | Execute a tool (code runner, API, file write) | | verify | Check a prior result for correctness or completeness | | delegate | Spawn a sub-agent or hand off to a specialist | | stop | Terminate the loop and return current state | ## Utility Function ``` U(a |...

Details

Author
athola
Repository
athola/claude-night-market
Created
6 months ago
Last Updated
today
Language
Python
License
MIT

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