SimScale polled the audience at its Agentic Unlocks event and came back with an unflattering result for its own market: “just 1 in 20” said they are moving AI from pilot into standard workflows. Seven in ten teams have not moved past informal experiments, and 37% have not started at all. Only 16% have AI embedded in daily work or are scaling it. Among teams that have started, more than twice as many are behind plan as ahead, at 20% against 9%, and 22% have defined no AI KPIs.
Treat this as what it is. More than 1,000 people registered for a vendor’s own AI event, the post gives no response count for any of the four questions, and an audience that signs up for an agentic engineering event is not a random sample of engineering. That bias should push adoption numbers up, which makes the low figures more interesting than the high ones. The 95% who named measurable value deserve more suspicion, particularly beside a 37% who have not started. The customer results are vendor-told too: Convion compressing a design loop from months to under one hour, within 5% of high-fidelity CFD. What survives is the direction of the finding, which the post supports with Capgemini’s remark that fewer than 10% of its clients think they have scaled AI, and the advice attached to it. “Do not start at the tool. Start with a record that you already own.”
The best line for API people comes from NVIDIA’s Todd McDevitt, who argued for determinism in the agent harness through typed tool calls, explicit permissions and physics checks. “It’s important to judge the system on its traces.” That is a statement about interfaces, and SimScale has real ones: 7 API pages, including the Geometry API, Mesh Operations API, Simulations API and Simulation Runs API. The catalog description records the same pipeline the post’s customers rely on, from geometry upload and mesh generation through setup, execution and results. Dexory’s repetitive setup, 198 contacts and 192 bolted connectors per tower section, is precisely the kind of job that sequence could carry. The post credits the SimScale agent with that work and does not say whether it runs through these APIs.
The measurement is where the harness argument comes home. SimScale’s Kin Score is 34.7, thin, with contract quality at 57.3 and discoverability at 66.1, but operational transparency at 10.5, contract governance at 13.6 and access clarity at 18.4. Agent Readiness is 17.9, agent-aware. Spec presence, agentic access, auth clarity and rate limit signals are lit. The agentic access profile records 21 operations, 10 of them acting. Error semantics, idempotency, dry-run mode and reversibility are unlit, and those are the properties that make a long simulation safe for an outside agent to retry or to judge afterward. SimScale’s own agent may have all of that internally. The public record does not show it.