Overview
Benchmarks measure configured coding-agent systems on selected workloads under particular oracles and resource protocols, not model ability in the abstract. This series traces harnesses and feedback loops, shows how task design and verifiers shape scores, contrasts task breadth with behaviorally grounded review evidence, and builds a decision-matched portfolio of slices, diagnostics, and operational signals. Its value is a bounded way to compare results and design evaluations while keeping unmeasured engineering and deployment properties visible.
Learning outcomes
- A benchmark score belongs to a configured system, task distribution, oracle, and resource protocol—not to the model alone.
- Task breadth reveals capability differences that a single bug-fixing pass rate can hide.
- Behavioral oracles test intended outcomes more directly than reference shape or wording, but remain bounded by what they encode.
- Useful evaluation portfolios combine end-to-end results with slices, diagnostics, protocol metadata, and delayed operational evidence.
Keywords
Preview every chapter · Unlock full text, audio, and video from chapter 4
PlansFAQ
What does a coding-agent benchmark score measure?
It measures how often a configured coding-agent system reaches an accepted state on a defined workload under a specified oracle and resource protocol. It does not, by itself, measure the model’s general software-engineering ability.
Why can the same model receive different coding-agent scores?
The same model can receive different scores because the harness changes prompts, tools, context, retries, environment handling, budget, or feedback. The result belongs to their interaction, not automatically to the model alone.
How should coding-agent benchmark results be compared?
Compare results by aligning—or explicitly reporting—the system, task distribution, oracle, protocol, and outcome. Otherwise, a percentage difference may reflect a changed workload or definition of success.
What makes a coding-agent evaluation useful for engineering decisions?
A useful evaluation combines end-to-end results with relevant task slices, behaviorally grounded oracles, protocol metadata, component diagnostics, and operational outcomes when available. The evidence should match the decision and preserve its limits.