/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.

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