abc-bench-benchmarking-agentic-backend-coding-in

name	abc-bench-benchmarking-agentic-backend-coding-in
title	ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development Scenarios
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2601.11077
keywords	["Agent","Benchmark"]
description	The evolution of Large Language Models (LLMs) into autonomous agents has expanded the scope of AI coding from localized code generation to complex, repository-level, and execution-driven problem solving. However, current benchmarks predominantly evaluate code logic in static contexts, neglecting the dynamic, full-process requirements of real-world engineering, particularly in backend development which demands rigorous environment configuration and service deployment. To address this gap, we intr...

Problem

ABC-Bench 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 abc-bench. 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/2601.11077
• Full PDF: https://arxiv.org/pdf/2601.11077
• HTML Version: https://arxiv.org/html/2601.11077

See the paper for comprehensive methodology, experimental protocols, benchmarks, and implementation details.