DecisionBench: A Benchmark for Emergent Delegation in Long-Horizon Agentic Workflows

2026-04-29 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

DecisionBench is a new benchmark substrate designed to evaluate emergent delegation in long-horizon agentic workflows. It standardizes a task suite including GAIA, τ-bench, and BFCL multi-turn, an 11-model peer pool from 7 vendor families, and a "call_model" delegation interface with an optional "read_profile" channel. The benchmark features a deterministic 7-skill annotation layer and a multi-axis metric suite covering quality, cost, latency, delegation rate, routing fidelity-at-k, vendor self-preference, and a counterfactual-delegation ceiling. A five-condition reference sweep across 23,375 task instances revealed that end-task quality is statistically flat across awareness conditions (|β|≤0.010, p≥0.21), highlighting the need for process-level metrics. Routing fidelity-at-1 varied from 7.5% to 29.5%, with on-demand tool access significantly outperforming preloaded descriptions. The study also identified a 15–31 percentage point gap between measured and perfect delegation, indicating substantial headroom for future orchestration methods.

Key takeaway

For MLOps Engineers designing agentic systems, relying solely on end-task quality to assess delegation effectiveness is insufficient and misleading. You should prioritize dynamic, on-demand peer information access via tools like "read_profile" over static preloaded descriptions, as this significantly improves routing fidelity. Focus on instrumenting process-level metrics to identify and capture the substantial 15-31 percentage point headroom available in current orchestration methods.

Key insights

Emergent delegation in LLM agents is best evaluated via process-level metrics, as end-task quality alone obscures orchestration signals.

Principles

• End-task quality alone is insufficient for evaluating agentic delegation.
• Information delivery channel impacts delegation more than content.
• Significant headroom exists for improving agent orchestration methods.

Method

DecisionBench fixes task suites, an 11-model peer pool, a "call_model" delegation interface, a 7-skill annotation layer, and a multi-axis metric suite to evaluate emergent delegation.

In practice

• Prioritize on-demand peer information access over preloaded descriptions.
• Implement process-level metrics like routing fidelity and delegation rate.
• Focus on improving delegation strategies to capture 15-31 pp headroom.

Topics

• DecisionBench
• LLM Agents
• Agentic Delegation
• Multi-axis Metrics
• Routing Fidelity
• Peer Models

Best for: Research Scientist, AI Architect, AI Engineer, AI Scientist, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.