1. The question benchmarks answer, and the question teams ask
++ A model leaderboard answers: given a fixed scaffold, which model scores highest? That is + the right question for a lab choosing a base model, and the wrong question for everyone + downstream. Downstream, the model is a config value. The product is the engine: the system + prompt, the tool set, the retrieval and context policy, the planning loop, the subagent + topology, and the guardrails. One survey puts the distinction plainly: evaluating the LLM + alone is like examining an engine on a stand, while agent evaluation must assess the whole + car under driving conditions [2]. +
++ The scale of what the scaffold contributes has been measured repeatedly. SWE-agent showed + in 2024 that the agent-computer interface alone moves resolve rates at a fixed model [24]. + Agentless showed the same year that a deliberately non-agentic pipeline could outperform + elaborate agents at a fraction of the cost [25]. And in 2026 a controlled factorial study + quantified it: across three models and three harness configurations on a 100-task + SWE-bench Verified subset, the harness contributed 18.48 pp² of score variance against the + model's 2.37 pp², a ratio of 7.8x, with six model-ranking reversals across nine harness + pairings [3]. The authors' conclusion is the thesis of this paper: the execution harness, + the infrastructure layer that governs context construction, tool interaction, + orchestration, and verification, is often a stronger determinant of agent performance than + the model it wraps [3]. +
++ The benchmark ecosystem has not caught up. The most comprehensive survey of agent + evaluation to date lists "Decoupling LLM & Harness Evaluation" as future work: most + current benchmarks conflate the two targets, and no established benchmark provides + controlled protocols that vary each factor independently [1]. The same survey finds cost + and efficiency metrics broadly neglected, echoing "AI Agents That Matter": accuracy-only + leaderboards drive needlessly costly agents [1, 23]. +
+ +2. Survey: coding-agent benchmarks
++ SWE-bench (Princeton/Stanford, 2023) established the template: 2,294 real + GitHub issues from 12 Python repos, graded by the repo's own FAIL_TO_PASS and PASS_TO_PASS + tests [4]. SWE-bench Verified (OpenAI, 2024) human-filtered 500 instances + to remove impossible or underspecified tasks [5]. SWE-bench Multimodal + adds visual, JavaScript-centric issues; SWE-bench Pro (Scale AI, 2025) + raises difficulty with long-horizon, enterprise-style tasks and a held-out commercial + subset [6]. SWE-bench Live (Microsoft Research and community, 2025) + attacks contamination directly with a monthly-refreshed feed of new issues [7]. All of + these grade one thing: did the repo's tests pass. None grades diff size, token spend, or + wall clock as a ranked metric, and their leaderboards mix (model, scaffold) pairs, so a + score is attributable to neither alone. They are, however, the raw material engine + benchmarking needs: real repos, real issues, executable verification. +
+
+ Terminal-Bench (Stanford and the Laude Institute, 2025) measures
+ terminal-native task completion in containers; its 2.0 release ships on
+ Harbor [8, 9]. Harbor deserves its own entry: it is a meta-harness from
+ the Terminal-Bench creators for "evaluating and optimizing agents and language models",
+ wrapping third-party benchmarks (Terminal-Bench 2.0, SWE-bench, Aider Polyglot) behind one
+ containerized runner, with the agent scaffold (Claude Code, OpenHands, Codex CLI, and
+ more) selected independently of the --model flag [9]. That structure enables
+ matched-model harness comparison, and a 2026 survey names Harbor (with Exgentic) as the
+ first frameworks pushing a unified protocol for general agent assessment [1]. What Harbor
+ does not do: rank efficiency, gate CI on regressions with statistics, or source fresh
+ tasks with reference traces. Harness-Bench (2026) is the academic
+ complement: a 6-harness by 8-model factorial over 106 sandboxed tasks (5,194 trajectories)
+ that fixes task, sandbox, budget, timeout, and evaluator while letting each harness keep
+ its native behavior [10]. It is a study rather than an ongoing service, but it is the
+ cleanest matched-model prior art to date.
+
+ Aider Polyglot (225 hard Exercism exercises) is honest about being a + model benchmark: one fixed harness, many models, with cost per run displayed [11]. + Commit0 (build a library from scratch against a spec and tests) and + RepoBench (repo-level completion) sit closer to the model end. + SWE-Gym and R2E are environment generators, aimed at + training rather than refereeing [12]. +
+ +3. Survey: general agent benchmarks
++ AgentBench (2023) spans eight environments from OS shells to web shopping + [13]. GAIA (Meta/HF, 2023) asks 466 tool-requiring questions with + unambiguous answers [14]. WebArena and VisualWebArena + grade functional task completion on self-hosted websites [15]; OSWorld + does the same for 369 tasks on real desktop operating systems [16]. + tau-bench and tau2-bench (Sierra) simulate + customer-service conversations with an LLM user and domain policies, and contribute the + ecosystem's best reliability statistic: pass^k, the probability that an agent succeeds on + all k independent attempts, which exposes flakiness a single-run pass rate hides [17]. + TheAgentCompany (CMU, 2024) drops agents into a simulated software + company (GitLab, RocketChat, ownCloud) and grades checkpointed long-horizon work with + partial credit [18]. BFCL (Berkeley) is the standard for function-call + correctness, including multi-turn tool use [19]; ToolBench covers + 16,000+ real APIs [20]. MLE-bench (OpenAI) uses 75 Kaggle competitions; + CORE-bench (Princeton) tests computational reproducibility of real + papers [21]. +
++ Two observations. First, nearly all of these publish leaderboards keyed by model, with the + benchmark's own reference scaffold underneath: they are model benchmarks with agentic + tasks. Second, the exceptions prove the demand: HAL, the Holistic Agent + Leaderboard (Princeton), re-runs (model, scaffold) pairs across many of the benchmarks + above and plots accuracy against cost, an explicit two-axis Pareto view [22]. HAL is the + closest thing to a public engine-aware leaderboard. It still is not a tool a team can + point at its own agent in its own CI, and cost is its only efficiency axis. +
+ +4. Survey: trace-based evaluation and eval platforms
++ Trajectory grading has real prior art. AgentBoard's progress rate + compares an agent's actual trajectory against an expected one for per-step progress + [2, 26]. T-Eval decomposes tool use into step-wise next-call alignment + [26]. LLM-as-judge trajectory scoring is now a stock feature of eval platforms. What does + not exist is what we will call a golden-trace corpus: reference traces from verified, + production-merged fixes, published as auditable evidence alongside outcome grades. +
++ The observability and eval platforms are where CI actually happens today. + LangSmith, Langfuse, Braintrust, + Arize Phoenix, and W&B Weave all offer tracing plus + dataset-driven evals, and most can fail a CI job on a metric drop [27]. + DeepEval runs evals as pytest tests; promptfoo runs + config-driven evals and red-team suites in CI; OpenAI Evals seeded the + genre [27]. On standards: OpenTelemetry's GenAI semantic conventions are emerging as the + de facto wire format for agent traces, and the Model Context Protocol showed that a small + interchange spec can reorganize an ecosystem within a year [28]. There is no equivalent + interchange standard for benchmark tasks, runs, or verdicts. A 2026 factorial study + proposes "Harness Cards", a structured disclosure taxonomy for exactly this reason [3]. +
++ The platforms' limitation is the mirror image of the benchmarks'. They grade your agent on + your dataset with judge-model rubrics: perfect for product regression, but with no + held-out verification (the eval data lives in your repo, next to the agent that will be + graded on it), no cross-engine comparability, and no shared task corpus, so a score means + nothing outside your org. +
+ +5. Best practices: what a credible agent benchmark must do in 2026
+-
+
- Separate the engine from the model, by construction. Hold one fixed while varying the other; stamp the run; refuse cross-attribution [1, 3, 10]. +
- Held-out verification. The grader must be absent from the workspace while the agent runs, and immune to anything the agent planted [4]. +
- Cost and efficiency as ranked metrics, not footnotes. Accuracy-cost Pareto curves, plus tokens, wall clock, and, we argue, diff size: no surveyed system ranks the size of the change an agent makes, though every reviewer knows a 4-line fix and a 400-line rewrite are not equal [22, 23, 2]. +
- Statistics that respect small n. Confidence intervals on rates; paired tests for head-to-heads; reliability metrics like pass^k; seeded, reproducible resampling [17, 23]. +
- Contamination control. Frozen sets decay into training data; live sourcing with recorded cutoffs is the credible answer [7]. +
- Receipts. Full transcripts, diffs, manifests, a reproduce command, and disclosure of the harness configuration [3]. +
- Run in CI. A benchmark you run at launch is marketing; a benchmark that fails a pull request is engineering. +
6. Gap analysis: the four capabilities, and who has them
+| Capability | Closest today | What's missing |
|---|---|---|
| (a) Matched-model engine isolation | +Harbor (structural) [9]; Harness-Bench, factorial study (research) [10, 3]; HAL (reporting) [22] | +An enforced, stamped property of every published run | +
| (b) Efficiency grading beyond cost | +HAL and Aider show cost [22, 11]; tau-bench measures reliability [17] | +Nobody ranks normalized git-diff size; tokens + wall clock rarely reported together | +
| (c) CI regression gating with statistics | +Eval platforms gate CI on judge-scored private datasets [27] | +No significance-aware gate over held-out, executable verification | +
| (d) Live tasks, verified fixes, golden traces | +SWE-bench Live sources fresh issues [7] | +Nobody promotes fixes upstream, uses maintainer merge as ground truth, or publishes the solving trace | +
No system does all four. That is the hole in the landscape.
+ +7. Arena, and the Agent Benchmark Protocol
++ Arena (github.com/macanderson/arena, + MIT) is our attempt at the first three capabilities, today: two or more coding agents on + the same tasks, same model, same budget, same timeout; held-out verification injected only + after the agent exits; Wilson intervals, exact McNemar on paired outcomes, and seeded + paired-bootstrap CIs on wall-clock, token, and cost deltas; no blended score; full + receipts with a one-line reproduce command; and a CI gate that fails a pull request only + when a regression clears interval noise. A Harbor adapter scales the same discipline to + SWE-bench Verified with the official containerized verifier [9]. +
++ The fourth capability needs a standard more than a product. The + Agent Benchmark Protocol defines four interchange schemas: a task + manifest, a run configuration, a trace format with per-subagent and per-model attribution, + and a verdict format with required statistics. On top of them it specifies a live + pipeline: a versioned registry of the top 500 open-source GitHub repositories; seeded + random draws of real backlog issues; fixes built and submitted upstream under each repo's + contribution rules; maintainer merge as verification; and the solving engine's full trace + published as a golden trace beside the frozen task. Replays are graded on resolution + first, then efficiency in strict order: smaller normalized diff, then fewer tokens, then + less wall clock, then lower cost. Golden traces are evidence and study material, + deliberately not the rubric, because trajectory-similarity grading punishes better routes + to a passing fix [26]. Contamination is handled by construction: tasks are drawn after + candidate model cutoffs, stamped, and retired from the scored set 180 days after their fix + goes public. +
++ Why a protocol rather than another leaderboard: the measurement problem is an + interoperability problem. Tasks, runs, traces, and verdicts need to move between task + authors, engines, harnesses, and auditors, exactly as context moves between hosts and + tools under MCP [28]. Arena is the reference implementation, not the standard; the + standard is the four schemas and the conformance suite. +
+ +8. Limitations
++ This survey characterizes fast-moving systems from their public documentation as of July + 2026; version specifics will drift. The 7.8x variance ratio comes from one study on one + benchmark family and one task size; it needs replication, though its direction agrees with + SWE-agent, Agentless, and Harness-Bench [3, 24, 25, 10]. Diff-size ranking is a proxy for + surgical precision, not design quality, and is honest only with the normalization and + disqualification rules the spec defines. And the author's conflict of interest is real: + the reader should treat Section 7 as a proposal to be attacked, with the receipts to + attack it. +
+ +References
+-
+
- Yehudai et al., Survey on Evaluation of LLM-based Agents, v2 (2026). arxiv.org/abs/2503.16416 +
- A Survey of AI Agent Evaluation (2025). arxiv.org/abs/2507.21504 +
- Harness variance factorial study: Harness Cards and the ETCSOVG taxonomy (2026). arxiv.org/abs/2605.23950 +
- Jimenez et al., SWE-bench (2023). arxiv.org/abs/2310.06770 +
- OpenAI, Introducing SWE-bench Verified (2024). openai.com/index/introducing-swe-bench-verified +
- Scale AI, SWE-bench Pro (2025). github.com/scaleapi/SWE-bench_Pro-os +
- SWE-bench Live (2025). swe-bench-live.github.io +
- Terminal-Bench (2025). tbench.ai +
- Harbor Framework (2026). github.com/harbor-framework/harbor +
- Harness-Bench (2026). arxiv.org/abs/2605.27922 +
- Aider Polyglot leaderboard. aider.chat/docs/leaderboards +
- SWE-Gym · R2E (r2e.dev) · Commit0 (github.com/commit-0/commit0) · RepoBench (2023) +
- Liu et al., AgentBench (2023). arxiv.org/abs/2308.03688 +
- Mialon et al., GAIA (2023). arxiv.org/abs/2311.12983 +
- Zhou et al., WebArena (2023). arxiv.org/abs/2307.13854 +
- Xie et al., OSWorld (2024). arxiv.org/abs/2404.07972 +
- Yao et al., tau-bench (2024). arxiv.org/abs/2406.12045 · tau2-bench: github.com/sierra-research/tau2-bench +
- Xu et al., TheAgentCompany (2024). arxiv.org/abs/2412.14161 +
- Berkeley Function-Calling Leaderboard. gorilla.cs.berkeley.edu/leaderboard.html +
- Qin et al., ToolLLM/ToolBench (2023). arxiv.org/abs/2307.16789 +
- OpenAI, MLE-bench (2024). arxiv.org/abs/2410.07095 · CORE-bench (Princeton, 2024) +
- HAL: Holistic Agent Leaderboard (Princeton). hal.cs.princeton.edu +
- Kapoor et al., AI Agents That Matter (2024). arxiv.org/abs/2407.01502 +
- Yang et al., SWE-agent (2024). arxiv.org/abs/2405.15793 +
- Xia et al., Agentless (2024). arxiv.org/abs/2407.01489 +
- Ma et al., AgentBoard (2024). arxiv.org/abs/2401.13178 · T-Eval (2023). arxiv.org/abs/2312.14033 +
- LangSmith · Langfuse · Braintrust · Arize Phoenix · W&B Weave · DeepEval · promptfoo · OpenAI Evals +
- Model Context Protocol (modelcontextprotocol.io) · OpenTelemetry GenAI semantic conventions +
+ The markdown source of this paper lives in the repo at
+ docs/agent-engine-benchmarks-2026.md. Corrections: open an issue with the
+ paper label.
+