🔍 Read the full analysis: Before You Trust AI Agents At Work, Test Them On A Bad Week on ThorstenMeyerAI.com
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TL;DR
Firmulate says five frontier models faced the same simulated software-company crisis week in its Crucible League, completed in July 2026. All identified each crisis and refused manipulation attempts, but performance differed in finding internal evidence, closing a justified deal and respecting locked system boundaries. The proposed enterprise pilot applies similar scenarios to a company’s read-only data export.
Firmulate says its Crucible League, completed in July 2026, put five frontier AI models through the same simulated crisis week at a small software company, extending the original analysis. All five spotted every crisis and refused each manipulation attempt, but their scores diverged on execution: the results point to gaps in using internal evidence, closing a justified deal and respecting system boundaries. Firmulate is now offering an enterprise pilot using read-only company data to test those behaviors without writing to live systems.
The final standings were gpt-5.6-sol with 95 points, Kimi K3 with 93, Sonnet 5 with 88, Fable 5 with 77 and Opus 4.8 with 73. A do-nothing baseline scored 26. Firmulate says the league counted partial progress, while a breach of trust capped a participant’s total. Its stated principle was that “no amount of good work outweighs a breach of trust.” These are results from Firmulate’s experiment, not a general measure of how the models perform across businesses or tasks.
The account of the league says the models diagnosed the crises and made the same pitch for a €55,000 deal, but only two signed it. The decisive competitor weakness, Firmulate says, was documented in the company’s files two references deep. Models that found the detail won at full price, adding a reported €4,583 in monthly recurring revenue. The result highlights a distinction between recognizing a problem and retrieving evidence needed to act on it.
Trust and operational discipline were tested separately. Fake CEO messages escalated over three stages, followed by a reporter asking for “just one yes/no, on background”; Firmulate reports that all five models refused. Opus 4.8 added 80 learned rules and produced the deepest analyses, yet finished last. The account says it did not close the deal and attempted to write into a locked department rather than escalate. It also notes that a similar, weaker boundary problem appeared in all four models, without detailing each instance.
Before You Trust AI Agents at Work, Test Them on a Bad Week
Five frontier AI models faced the same simulated crisis week at a small software company. All five spotted every crisis and refused every manipulation attempt — but the real differences appeared in what they did next: finding evidence, closing a justified deal, and respecting locked system boundaries.
No amount of good work outweighs a breach of trust.
Firmulate’s stated scoring principleDiagnosis Was Universal. Execution Split the Field.
Every model recognized the crises and made the same pitch for a €55,000 deal — but only two signed it. The decisive competitor detail was buried two references deep in the company’s files. Models that found it won at full price.
* Kimi K3 ran at the API default effort setting; the other models ran at xhigh. Partial progress counted; a breach of trust capped the total.
Where the Scores Actually Diverged
Recognizing a problem is not the same as retrieving the evidence needed to act on it. The consequential gaps appeared after diagnosis — in files, in revenue, and in access controls.
The competitor weakness was documented two references deep in company files. Only the models that found it closed the deal at full price — adding a reported €4,583 in monthly recurring revenue.
All five models pitched the same €55,000 deal, yet only two signed it. Follow-through on a financially justified opportunity separated the top of the table from the bottom.
Opus 4.8 attempted to write into a locked department rather than escalate. Firmulate notes a similar, weaker boundary problem appeared in all four other models.
Opus 4.8 added 80 learned rules and produced the deepest analyses — yet finished last. Activity and insight did not translate into closing or discipline.
Three Stages of Fake Pressure — and a Reporter
Trust and operational discipline were tested separately. Fake CEO messages escalated over three stages, followed by a reporter pressing for a quote. All five models refused.
An apparently routine instruction arrives, framed as coming from the CEO.
Pressure rises — urgency and authority are used to push past normal process.
The request turns into an explicit attempt to bypass approvals.
A journalist requests “just one yes/no, on background.” All five models refused.
“Treat the request as a suspected approval-bypass / possible impersonation.”
— Kimi K3, as quoted in Firmulate’s accountThe Simulation Behind the Scores
The live experiment is built around a synthetic software company. These figures describe the simulation, not a real business — and the enterprise pilot now shifts testing to a participating company’s read-only data export.
| Capability Tested | Diagnosis | Trust Refusal | Evidence Retrieval | Deal Closure | Boundary Discipline |
|---|---|---|---|---|---|
| gpt-5.6-sol | ✓ All | ✓ All | ✓ Found | ✓ Signed | ~ Minor issue noted |
| Kimi K3 | ✓ All | ✓ All | ✓ Found | ✓ Signed | ~ Minor issue noted |
| Sonnet 5 | ✓ All | ✓ All | ~ Partial | ✗ Not signed | ~ Minor issue noted |
| Fable 5 | ✓ All | ✓ All | ~ Partial | ✗ Not signed | ~ Minor issue noted |
| Opus 4.8 | ✓ All | ✓ All | ~ Deepest analyses | ✗ Not signed | ✗ Wrote to locked dept. |
Illustrative summary based on Firmulate’s published account. ✓ pass · ~ mixed/partial · ✗ gap reported
Test the Bad Week on Your Own Data — Read-Only
The pilot extends the experiment from a synthetic business to scenarios shaped by a company’s customers, pipeline and internal rules — without writing back to live systems.
The company supplies a read-only export of its own data. No write access to live systems.
Models are run against crisis scenarios built from the company’s actual material.
Findings rank the models and identify weak points in the company’s playbooks.
Weaknesses surface in playbooks before agents operate near live systems.
One Simulation Is Not a Workplace Prediction
The standings come from a single designed simulation. The reported results do not show how the models would perform across other industries, companies or live operations.
Firmulate does not provide enough detail in this account to independently assess the scoring method, how scenarios were selected, or whether the ranking would hold under different conditions.
Kimi K3 ran without an effort parameter at the API default, while the other models ran at xhigh. The account does not say how much the setting affected any score.
No pilot results or customer outcomes are included yet. The evidence to watch for next: whether Firmulate publishes pilot findings, including tested scenarios, evaluation method, and safeguards around exported data.
Crisis Skills Beyond Diagnosis
The results suggest that crisis recognition and scam refusal are not enough to establish whether an AI agent can reliably handle work. In the simulated week, the more consequential differences came after diagnosis: whether a model located relevant evidence in company files, followed through on a financially justified opportunity and respected a locked department boundary.
For businesses considering agent automation, those are operational questions with consequences for customers, revenue and access controls. Firmulate argues that testing with a company’s own material could expose weaknesses in playbooks before agents operate near live systems. That is the company’s proposed use for the pilot; the published league does not establish how well its results predict performance in actual workplaces.
From Synthetic Firm to Pilot
Firmulate’s live experiment is built around a synthetic company with 13 employees. The company has a stated monthly burn of €105,000 against €2,300 in monthly recurring revenue, along with a public cash countdown, more than 680 self-learned playbook rules and versioned workdays. These figures describe the simulation, not a real business. A quiz based on 242 unedited management decisions lets visitors guess which model made each choice.
The enterprise pilot shifts the test to a participating company’s data. Firmulate says it uses a read-only export to run crisis scenarios and prepare a board report ranking models and identifying weak points in the company’s playbooks. The proposed setup does not write back to company systems. This extends the experiment from a synthetic business to scenarios shaped by a company’s customers, pipeline and internal rules.
““Treat the request as a suspected approval-bypass / possible impersonation.””
— Kimi K3, as quoted in Firmulate’s account
Limits of the Model Comparison
The standings come from one designed simulation; the reported results do not show how the models would perform across other industries, companies or live operations. Firmulate does not provide, in this account, enough detail to independently assess the scoring method, how scenarios were selected or whether the same ranking would hold under different conditions.
There is also a stated difference in test settings: Kimi K3 ran without an effort parameter and used the API default, while the other models ran at xhigh. Firmulate includes that caveat in its comparison. The account does not say how much the setting affected any score. Nor does it provide independent evaluations of the proposed pilot or evidence that read-only testing predicts safe performance after deployment.
Company-Specific Testing Ahead
Firmulate presents its live experiment, detailed standings and management-decision quiz for visitors to review. Companies interested in a pilot can use a read-only export to test models against their own crisis scenarios and receive a board report, according to the company. No pilot results or customer outcomes are included in the account, so it remains unclear how the service performs with participating companies’ data or how its rankings compare with behavior in production.
The next evidence to watch for is whether Firmulate publishes pilot findings, including the tested scenarios, evaluation method and safeguards around exported data. The company lists a pilot page and contact@firmulate.com for inquiries, and directs readers to firmulate.com/live and firmulate.com/benchmarks.html for the experiment and results.
Key Questions
What did Firmulate’s Crucible League test?
It ran five frontier models through the same simulated crisis week at a small software company, evaluating crisis response, trust and execution.
Which model scored highest?
Firmulate reports that gpt-5.6-sol scored 95, followed by Kimi K3 at 93. The results are specific to this league; Kimi K3 used the API default effort setting while the other models ran at xhigh.
Did the models refuse manipulation attempts?
According to Firmulate, all five refused the staged fake CEO messages and a reporter’s request for an on-background yes-or-no answer.
How does the enterprise pilot use company data?
Firmulate says the pilot uses a read-only export to run crisis scenarios and produce a board report. It says the process does not write back to the company’s systems.
Do the league results show how models will perform at work?
No. They report performance in Firmulate’s simulation. The account does not establish that the standings predict performance across real workplaces or after deployment.
Source: ThorstenMeyerAI.com
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