Benchmarking ai Agents Beyond Models

Problems It Solves

• Benchmark mismatch — A model that scored 78% in one harness scored 42% in another on the same task. Without a framework for separating harness contribution from model contribution, that gap is invisible and the wrong procurement decision gets made.

• Task type blindness — Most benchmarks use code generation tasks. If your team's work is multi-session, multi-step, or tool-dependent, the benchmark score literally does not apply. This skill shows you how to match benchmark task type to your actual task distribution.

• System comparison disguised as model comparison — Nearly all published comparisons swap both the model and the harness simultaneously, then credit the model. This skill gives you the questions to ask and the protocol to run when you need to know what the model actually contributes.

• Isolated evaluation deployed in a harness — A model evaluated via raw API behaves differently than the same model running inside a harness with context management, memory, and tool access. Isolation benchmarks systematically underestimate harness-integrated performance and mislead deployment planning.

What You Get

The skill delivers a complete harness-aware evaluation system:

• The performance decomposition model — production performance = model capability × harness multiplier, with a breakdown of the five harness dimensions that constitute the multiplier: context management, tool integration depth, memory continuity, verification mechanisms, and multi-agent coordination.

• Four benchmark interpretation questions — A structured checklist for auditing any published comparison before treating its headline as a performance prediction.

• The Harness-Aware Evaluation Protocol — A five-step method (representative task set definition → harness-constant comparison → task-level outcome measurement → harness dimension scoring → system-level report) for running evaluations that will predict your team's actual results.

• A system-level performance report template — A structured artifact capturing task completion rate, bug rate, verification pass rate, session restart overhead, and harness multiplier observed — with a benchmark correlation section that closes the loop between what vendors claim and what you measured.

• Anti-pattern library — Three named anti-patterns with concrete fixes: benchmarking in isolation, reading benchmark headlines without harness footnotes, and attributing all performance gains to model improvements.

Who Should Use This

• Engineering and platform teams evaluating AI coding agent procurement decisions who are working from published benchmark scores that may not predict behavior in their environment.

• Technical leads whose team's agent is underperforming relative to benchmark expectations — and who need a structured method to identify whether the gap is model, harness, or task mismatch.

• Engineering managers and CTOs who need to present an evidence-based agent procurement recommendation to leadership without being misled by vendor-controlled benchmark comparisons.