Large Language Models (LLMs) based autonomous agents demonstrate multifaceted capabilities to contribute substantially to economic production. However, existing benchmarks remain focused on single agentic capability, failing to capture long-horizon real-world scenarios. Moreover, the reliance on human-in-the-loop feedback for realistic tasks creates a scalability bottleneck, hindering automated rollout collection and evaluation. To bridge this gap, we introduce AgencyBench, a comprehensive be...
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---
name: agencybench-benchmarking-the-frontiers-of
title: "AgencyBench: Benchmarking the Frontiers of Autonomous Agents in 1M-Token Real-World Contexts"
version: 0.0.2
engine: skillxiv-v0.0.2-claude-opus-4.6
license: MIT
url: "https://arxiv.org/abs/2601.11044"
keywords: [Agent, Benchmark]
description: "Large Language Models (LLMs) based autonomous agents demonstrate multifaceted capabilities to contribute substantially to economic production. However, existing benchmarks remain focused on single agentic capability, failing to capture long-horizon real-world scenarios. Moreover, the reliance on human-in-the-loop feedback for realistic tasks creates a scalability bottleneck, hindering automated rollout collection and evaluation. To bridge this gap, we introduce AgencyBench, a comprehensive bench..."
---
## Problem
AgencyBench addresses key challenges in autonomous agent development. This paper provides solutions for evaluating, building, or improving agent systems.
## Key Approach
The paper introduces a novel framework, methodology, or benchmark for agencybench. The core contributions include:
1. Systematic framework or benchmark for agent evaluation and development
2. Empirical findings on agent performance, efficiency, or capabilities
3. Generalizable principles applicable across domains
## When to Use
Use this skill when you need to:
- Evaluate or benchmark autonomous agent systems
- Understand best practices in agent design and evaluation
- Learn empirical results on agent performance
- Improve agent efficiency, reasoning, or capabilities
## When NOT to Use
- For non-agent-related tasks
- When seeking quick implementation code (see the paper for details)
- For general knowledge unrelated to autonomous agents
## Resources
- ArXiv Abstract: https://arxiv.org/abs/2601.11044
- Full PDF: https://arxiv.org/pdf/2601.11044
- HTML Version: https://arxiv.org/html/2601.11044
See the paper for comprehensive methodology, experimental protocols, benchmarks, and implementation details.
