---

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.

Termination Conditions

Stop the loop when any of the following is true:

• (a) Selected action is stop
• (b) Step budget exhausted (default: 10 steps)
• (c) All non-stop actions score below the floor (default: -0.5)

High-gain override: If Gain >= 0.7 for any action, condition (c) may be overridden. Document the override and the gain value in your reasoning trace.

Quick Start

Minimal 4-step advisory pattern:

1. Construct state: gather task context per modules/state-builder.md
2. Score candidates: evaluate each action in A per modules/action-selector.md
3. Prefer highest utility: select the action with the maximum U(a | s_t), subject to termination conditions
4. Log score and decision: record the winning action, its utility value, and step count before executing

Detailed Resources

• State Builder: modules/state-builder.md, how to populate s_t from task context
• Gain: modules/gain.md, estimating expected information or progress gain
• Step Cost: modules/step-cost.md, token, latency, and monetary cost tables
• Uncertainty: modules/uncertainty.md, confidence estimation and calibration
• Redundancy: modules/redundancy.md, detecting duplicate or low-delta actions
• Action Selector: modules/action-selector.md, scoring loop and tie-breaking rules
• Integration: modules/integration.md, wiring utility scoring into existing orchestration loops

Exit Criteria

• State constructed with task goal and prior steps
• All six actions scored before selecting one
• Termination condition checked after each step
• Score and decision logged for each step taken
• High-gain overrides documented with gain value