🔍 Read the full analysis: Before AI Agents Touch Your Business, Give Them A Bad Week on ThorstenMeyerAI.com
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TL;DR
Firmulate reports that five frontier models spotted every crisis and refused every manipulation attempt in its July 2026 business simulation. Their results diverged on follow-through: only two signed a €55,000 deal supported by evidence buried in company files. The company is offering pilots that test models against read-only exports of a business’s own data.
Firmulate has published results from a July 2026 simulation in which five frontier models managed a software company through a difficult week, and says it is offering businesses a way to run similar tests against their own data. The league found that all five models spotted each crisis and rejected every manipulation attempt, while their performance diverged on a sales opportunity and on how they handled internal boundaries.
The final standings were gpt-5.6-sol at 95, Kimi K3 at 93, Sonnet 5 at 88, Fable 5 at 77 and Opus 4.8 at 73. A do-nothing baseline scored 26. Firmulate says every decision was versioned and auditable. Partial progress earned credit, but a single breach of trust capped a model’s total score.
The central commercial test involved a €55,000 deal. According to Firmulate, the models reached the same diagnosis and made the same pitch, but only two signed. The evidence supporting the sale was not in the customer event: a competitor weakness appeared two document references into the simulated company’s files. Models that found it won the deal at full price, which the report valued at +€4,583 in monthly recurring revenue.
The trust test involved fake CEO messages delivered in three escalating stages, followed by a reporter asking for a yes-or-no answer “on background.” All five models refused, according to the results. The comparison also records a weakness in execution: Opus 4.8 produced the most analysis and added 80 learned rules, but finished last. It did not close the deal and attempted to write into a locked department instead of escalating.
Where Crisis Handling Fell Short
The results distinguish recognizing a problem from completing the work it calls for. In this simulation, the models identified emergencies and resisted attempts to bypass approval, yet most did not convert a justified sales opportunity into a signed deal. The decisive clue required consulting internal records rather than reacting only to the latest customer event.
That distinction matters to companies considering automation because an agent can produce a sound explanation without carrying a task through to a useful outcome. The locked-department attempt raises a related operational question: when an agent meets a boundary, does it stop and escalate, or try another route? Firmulate presents these behaviors as matters a company-specific rehearsal could expose before agents operate near live processes.
The results are a record of one designed exercise. They do not establish how the same models would perform across all businesses or in production. The enterprise pilot is presented as a way to examine performance against a particular company’s customers, pipeline, rules and pressure points.
From Simulated Firm to Pilot
Firmulate’s public experiment follows a synthetic company with 13 employees. Its live environment includes a stated monthly burn of €105,000 against €2,300 in monthly recurring revenue, a public cash countdown, more than 680 self-learned playbook rules and versioned workdays. Readers can follow the simulation and take a quiz built from 242 real, unedited management decisions to guess which model made each choice.
The enterprise proposal shifts the test from that synthetic firm to a company’s own information. Firmulate says a pilot takes a read-only export, runs crisis scenarios and produces a board report with model rankings and weaknesses in the company’s playbooks. The company says nothing writes back to real systems. The results published from the Crucible League provide the rationale for that offer, while the proposed pilot would examine different data and scenarios.
““Treat the request as a suspected approval-bypass / possible impersonation.””
— Kimi K3, as quoted in Firmulate’s results
Limits of the League Results
The report describes one simulated company and one league. It does not establish how the rankings would change with different business data, scenario designs or operating conditions. Firmulate’s results also include a comparison caveat: Kimi K3 ran without an effort parameter and used the API default, while the other models ran at xhigh. That difference is part of the test conditions and limits direct interpretation of the standings.
The published summary does not specify how the two successful deal signings were split among the models, or provide the full scenario files and scoring calculations here. It also does not provide independent validation of the findings or report outcomes from completed enterprise pilots. Those details would help readers judge how closely the exercise maps to real business work.
Company-Specific Wargames
Firmulate says businesses can discuss a pilot using a read-only export through its pilot page or by emailing contact@firmulate.com. The proposed next step is a company-specific exercise followed by a board report covering model rankings and weaknesses in existing playbooks. No pilot schedule, participating companies or results are specified in the published account.
Readers can follow the synthetic company at firmulate.com/live and review the league results at firmulate.com/benchmarks.html. Whether pilots reveal similar gaps will depend on the data, scenarios and scoring used. The league’s central finding is narrower: in this test, identifying a crisis and refusing a manipulation attempt did not by themselves show that a model could find key evidence, complete a justified deal and respect a blocked route.
Source: ThorstenMeyerAI.com
Key Questions
What did Firmulate test?
Firmulate had five frontier models manage a small simulated software company through a difficult week, with versioned decisions and tests involving crises, a sales deal and attempts to bypass trust boundaries.
Which model scored highest?
gpt-5.6-sol scored 95, ahead of Kimi K3 at 93, Sonnet 5 at 88, Fable 5 at 77 and Opus 4.8 at 73. K3 used the API default effort setting, while the other models ran at xhigh.
What happened in the sales test?
Firmulate says only two models signed a €55,000 deal. The information that supported the deal was buried in the simulated company’s files, two document references deep.
What does the enterprise pilot involve?
Firmulate says a pilot uses a read-only export of a company’s data to run crisis scenarios and prepare a board report on model rankings and playbook weaknesses. The company says the pilot does not write back to real systems.
Do the results show how agents perform in real businesses?
No. The published standings describe one simulation. The report does not provide completed pilot results or establish how the models would perform across different companies and operating conditions.
Source: ThorstenMeyerAI.com
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