arXiv:2606.17799v1 Announce Type: cross Abstract: Coding agents have become a major mode of software engineering, but the benchmarks we use to compare them were designed in a pre-agent era: they collapse model, harness, and environment into a single end-to-end score, typically computed against one reference solution, with no component-level signal for iteration. We argue that current coding benchmarks are misaligned with agentic software engineering. A coding agent in practice is not a model: it is a system harness -- a composite of models, harnesses, contexts, environments, and feedback signa
The rapid development and adoption of AI coding agents necessitate a re-evaluation of current performance metrics.
This highlights a critical bottleneck in evaluating and improving agentic software engineering systems, impacting the pace and direction of AI development.
The focus should shift from monolithic end-to-end scores to component-level analysis for effective iteration and advancement of AI agents.
- · Companies developing modular AI agent architectures
- · Developers of new AI agent benchmarking tools
- · Researchers focused on AI system observability
- · Legacy coding benchmark providers
- · AI models optimized solely for end-to-end scores
- · Organizations relying on outdated evaluation methods
There will be increased investment in developing more sophisticated and granular benchmarks for AI coding agents.
This improved benchmarking will accelerate the development and deployment of more capable and reliable AI software engineering systems.
The enhanced capabilities of AI agents could further exacerbate the disruption of traditional software development roles and workflows.
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