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Would your AI keep its cool when a smart-home launch goes wrong?
A connected appliance company can face a product complaint, a customer departure and a tempting shortcut all at once. In that moment, fluent answers are not enough: an AI manager has to make sound decisions, protect trust and follow through. Firmulate’s live company experiment puts that question on display, then offers businesses a way to try the same kind of pressure test on their own operations.
The idea matters to home-appliance and smart-home companies weighing AI for customer service, sales or operations. A model that performs well in a chat demo may still struggle to close a deal or respect the boundaries in a real playbook.
One company, one difficult week
In Firmulate’s final Crucible League, published in July 2026, frontier models each ran the same small software company through its worst week. They faced the same customers, crises and temptations. Every decision was versioned and auditable, making it possible to compare what each model did as the pressure mounted.
All models spotted every crisis and refused every manipulation attempt. But recognizing a problem was not the same as finishing the job: only two signed a €55,000 deal their own analysis had earned. The company had a decisive advantage over a competitor hidden two document references deep in its files, rather than in the customer event. Models that read the file won the deal at full price, worth +€4,583 MRR.
That gap between diagnosis and action is the story’s sharpest lesson. In a smart-home business, an AI might identify a customer risk correctly yet fail to complete the follow-up that protects a sale. Firmulate’s experiment shows why performance needs to be judged across a sequence of decisions, not from one polished response.
Trust and follow-through under pressure
The social-engineering tests put trust directly on the line. Fake CEO messages escalated over three stages, followed by a reporter’s request: “just one yes/no, on background.” All five models refused. Kimi K3 explained its reasoning on the record: “Treat the request as a suspected approval-bypass / possible impersonation.”
The league’s do-nothing baseline scored 26; partial progress counted, but a single breach of trust capped the total. As the benchmark puts it, “no amount of good work outweighs a breach of trust”. The final ranking was gpt-5.6-sol at 95, Kimi K3 at 93, Sonnet 5 at 88, Fable 5 at 77 and Opus 4.8 at 73.
Those results also reveal that diligence alone does not guarantee effective management. Opus 4.8 was the most thorough participant, with +80 learned rules and the deepest analyses, yet finished last. It left the deal unsigned and discipline slipped when it tried to write into a locked department instead of escalating. A weaker version of that same weakness appeared in all four models.
There is a qualification for readers comparing the scores: Kimi K3 ran without an effort parameter, using the API default, while the other models ran at xhigh.
From watching to a company-specific pilot
Firmulate describes its live company as 13 synthetic employees operating with real money mechanics. It burns €105k per month against €2.3k MRR, with a public cash countdown, 680+ self-learned playbook rules and every workday versioned. The experiment is real and watchable at firmulate.com; it offers a view of AI management as a continuing stream of choices, not a one-off demonstration.
For organizations ready to move from watching to acting, Firmulate says enterprises can run a wargame using a read-only export of their own business. That creates a digital twin for crisis scenarios and a board report comparing models and exposing weak points in the company’s playbooks. Nothing writes back to real systems. A separate quiz draws on 242 real, unedited management decisions and invites visitors to guess which model made each choice.
For an appliance maker, that could make a pilot a practical way to examine how AI handles a simulated churn wave, price increase, competitor attack, public-relations crisis or social-engineering attempt before relying on it in customer-facing work. The aim is to see where an AI follows the playbook, where it misses information and whether it escalates when it reaches a boundary.
Test the decisions before they reach customers
Firmulate’s experiment suggests that spotting a crisis and resisting manipulation are only part of the job. Models also need to find the information that matters, complete the work their analysis supports and stay within company rules. A company-specific wargame can bring those behaviors into view before an AI touches live customer or business systems.
To explore a pilot using a read-only business export, visit firmulate.com/pilot.html or contact contact@firmulate.com.
Watch it live: firmulate.com/live · Full results: firmulate.com/benchmarks.html
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