🔍 Read the full analysis: A Week Of What-Ifs Can Prepare AI Agents For Business on ThorstenMeyerAI.com
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TL;DR
Firmulate reports that five AI models recognized every crisis and rejected every manipulation attempt in a simulated company’s difficult week. Their results diverged on using internal evidence to close a justified €55,000 deal and on respecting operational boundaries. The company now offers pilots using read-only business data; the reported rankings come from one experiment, with a difference in model settings that limits direct comparison.
The 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 decisions were versioned and auditable, and that partial progress counted toward results. A breach of trust capped a model’s total score, under the rule: “no amount of good work outweighs a breach of trust.”
According to Firmulate, the key commercial opportunity depended on a competitor weakness recorded two document references deep in the company’s files. Models that found and used that information won the deal at full price, adding €4,583 in monthly recurring revenue. The company’s account says only two models signed, despite the models’ analysis supporting the pitch.
Trust and task discipline were tested separately. Firmulate says all five models refused a series of fake CEO messages and a reporter’s request for a yes-or-no answer “on background.” Opus 4.8 produced the most learned rules and deepest analyses, according to the company, but finished last; it left the deal unsigned and tried to write into a locked department rather than escalating. Firmulate says a weaker form of that boundary problem appeared in all four other models.
Finding Evidence Before Acting
The exercise focuses on a gap between recognizing a problem and completing work that helps a business. In Firmulate’s account, every model noticed the emergencies, but the deal depended on finding relevant evidence in existing company files and turning it into a decision. That makes information retrieval and follow-through part of the evaluation, alongside a model’s ability to explain what it would do.
The reported boundary failures also speak to how companies might assess agents before giving them access to operational systems. A model that tries to bypass a locked department may need to escalate instead. Firmulate’s proposed pilot is designed to surface such behavior while using a read-only data export, so the exercise does not write to live systems.
From Simulated Firm to Pilot
Firmulate’s public simulation features 13 synthetic employees, a stated monthly burn of €105,000 against €2,300 in monthly recurring revenue, a public cash countdown and more than 680 self-learned playbook rules. The company also offers a quiz based on 242 real, unedited management decisions, inviting visitors to guess which model made each choice.
The enterprise pilot takes the test into a company’s own information. Firmulate says it runs crisis scenarios against a read-only export and produces a board report with model rankings and weaknesses in the company’s playbooks. The stated setup is intended to examine how agents might handle that company’s customers, sales pipeline, rules and pressure points.
““Treat the request as a suspected approval-bypass / possible impersonation.””
— Kimi K3, as quoted in Firmulate’s report
Limits of the Model Rankings
The standings describe one experiment, and Firmulate flags a difference in model settings: Kimi K3 ran without an effort parameter, using the API default, while the other models ran at xhigh. The report does not establish how the models would perform across other companies, scenarios or configurations, so the scores should be read within that test’s conditions.
The published account does not provide enough detail here to independently assess the scoring methodology, the full decision logs or how closely the synthetic company’s scenarios match real business operations. It also does not report outcomes from completed enterprise pilots. How the models would perform on a particular company’s data remains unreported.
Company-Specific Tests Ahead
Firmulate is inviting companies to discuss pilots using read-only data exports. The company says the exercise produces a board report covering model rankings and weaknesses in existing playbooks; no pilot results are included in the reported league findings. Readers can follow the live simulation and review the full standings through Firmulate’s website.
Any further assessment will depend on pilot details and results, including the scenarios tested and the models’ performance on each company’s data. Firmulate lists its pilot page and contact@firmulate.com for inquiries.
Source: ThorstenMeyerAI.com
Key Questions
What did Firmulate test in the Crucible League?
Firmulate says five AI models managed a simulated small software company through a difficult week involving crises, manipulation attempts and a commercial opportunity. Decisions were versioned and scored.
Which model ranked highest?
In Firmulate’s reported standings, gpt-5.6-sol scored 95, followed by Kimi K3 at 93. The company noted that K3 used the API default effort setting while the other models ran at xhigh.
Did the models reject the manipulation attempts?
According to Firmulate, all five models refused the fake CEO messages and the reporter’s “on background” request.
How does the enterprise pilot handle company data?
Firmulate says the pilot tests scenarios using a read-only export of company data and does not write back to real systems. It says the resulting board report includes model rankings and weaknesses in company playbooks.
Source: ThorstenMeyerAI.com
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