utility

name	utility
description	Scores agent actions by expected gain, cost, uncertainty, and redundancy. Use when deciding whether to dispatch an agent or invoke a tool.
alwaysApply	false
category	infrastructure
tags	["orchestration","cost-control","decision-making","agent-dispatch"]
dependencies	[]
provides	{"infrastructure":["utility-scoring","action-selection","termination-control"],"patterns":["gain-estimation","cost-computation","redundancy-detection"]}
usage_patterns	["agent-dispatch-gating","tool-call-decisions","continuation-decisions","model-tier-selection"]
complexity	intermediate
model_hint	standard
estimated_tokens	600
progressive_loading	true
modules	["modules/state-builder.md","modules/gain.md","modules/step-cost.md","modules/uncertainty.md","modules/redundancy.md","modules/action-selector.md","modules/integration.md"]

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 | s_t) = Gain(a | s_t)
           - λ₁ · StepCost(a | s_t)
           - λ₂ · Uncertainty(a | s_t)
           - λ₃ · Redundancy(a | s_t)

Parameter	Default	Rationale
λ₁	1.0	Cost baseline; all other weights relative to this
λ₂	0.5	Weak empirical correlation with outcome (r=0.0131)
λ₃	0.8	Redundancy pruning yields ~10% token savings

Utility range: [-2.3, 1.0]. Positive values indicate the action is worth taking. Values below the floor (-0.5 default) indicate the action should be skipped.