---
name: utility
description: 'Score candidate agent actions by expected gain, cost, uncertainty, and redundancy to guide dispatch and termination decisions.'
version: 1.9.0
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