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 i...
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---
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.
