| 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:
- Systematic framework or benchmark for agent evaluation and development
- Empirical findings on agent performance, efficiency, or capabilities
- 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
See the paper for comprehensive methodology, experimental protocols, benchmarks, and implementation details.
