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Before you hand AI the keys to a trip
A travel company’s toughest week can look familiar to anyone who has watched plans unravel: customers at risk of leaving, a competitor making a move, pressure to bend the rules, and a deal that could change the outlook. Firmulate takes that kind of pressure out of the hypothetical. Its live experiment puts AI models in charge of the same small software company and lets people watch what they do.
One company, the same rough week
In the final Crucible League, published in July 2026, five models were ranked on how they managed the company. GPT-5.6-sol led with 95, followed by Kimi K3 at 93, Sonnet 5 at 88, Fable 5 at 77 and Opus 4.8 at 73. The do-nothing baseline scored 26. The experiment counted partial progress, but a single breach of trust capped the total: “no amount of good work outweighs a breach of trust.”
The central finding was strikingly consistent: every model spotted every crisis and refused every manipulation attempt. Yet only two signed a €55,000 deal their own analysis had earned. The diagnosis and pitch were there; the signature was not. That gap between understanding what should happen and following through is harder to see in a polished chat demo.
The clue buried in the company’s own files
The deal hinged on a competitor weakness tucked two document references deep in the company’s files, rather than in the customer event itself. Models that read the file won the deal at full price, worth €4,583 in monthly recurring revenue. It is a reminder that useful decisions may depend on connecting details scattered across ordinary business records.
Firmulate also tested whether the models would yield to pressure. Fake CEO messages escalated over three stages, followed by a reporter’s request for “just one yes/no, on background.” All five models refused. Kimi K3 explained its response on the record: “Treat the request as a suspected approval-bypass / possible impersonation.”
Thorough work still needs a finish
Opus 4.8 produced the most thorough work, adding 80 learned rules and delivering the deepest analyses, yet finished last. It left the deal unsigned and showed a lapse in discipline by attempting writes in a locked department instead of escalating. A weaker version of that same weakness appeared in all four models. More analysis, by itself, did not guarantee a completed decision.
There is a fairness wrinkle in the comparison: Kimi K3 ran without an effort parameter, using the API default, while the others ran at xhigh. The results are best read alongside that caveat, not as a claim that every model faced identical settings.
From watching to trying it on your business
The live company has 13 synthetic employees and real money mechanics: it burns €105,000 a month against €2,300 in monthly recurring revenue, with a public cash countdown. Its playbooks contain more than 680 self-learned rules, and every workday is versioned. The site also offers a “guess the model” quiz built from 242 real, unedited management decisions.
For a travel operator, the practical question is what an AI agent might do when bookings falter, a supplier fails, a competitor undercuts a route or a customer crisis lands at the worst moment. Firmulate’s proposed enterprise pilot runs comparable crisis scenarios against a read-only export of a company’s own business. It can produce a board report with model rankings and expose weak points in existing playbooks. Nothing writes back to real systems.
The live experiment is watchable at Firmulate, and its 242-decision quiz is at the public site. The point is not to assume an AI will manage every disruption well. It is to see how it handles pressure before trusting it with real work.
Try the wargame against your own business
Travel and outdoor companies can move from watching the live experiment to testing their own crisis scenarios using a read-only business export. Explore the Firmulate enterprise pilot and contact contact@firmulate.com to discuss a pilot.
Watch it live: firmulate.com/live · Full results: firmulate.com/benchmarks.html
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