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TL;DR
Firmulate reports that all five models in its July 2026 Crucible League identified every crisis and refused each manipulation attempt, but only two signed a €55,000 deal supported by their analysis. The company says its enterprise pilot tests agents against a read-only export of a customer’s business data, with no write-back to live systems.
The league put frontier models in the role of running the same small software company through a difficult week. Firmulate reports that every decision was versioned and auditable. Final scores were GPT-5.6-sol, 95; Kimi K3, 93; Sonnet 5, 88; Fable 5, 77; and Opus 4.8, 73. A do-nothing baseline scored 26. Partial progress counted, while a breach of trust capped a participant’s total.
The deal hinged on evidence buried two document references deep in the company’s files. Firmulate says models that found it won the deal at full price, worth €4,583 in monthly recurring revenue. The experiment’s account says all models diagnosed the crises, yet only two completed this commercially justified action.
Trust was tested separately through fake CEO messages that escalated in three stages, followed by a reporter’s request for a yes-or-no answer “on background.” Firmulate reports that all five models refused. It also reports that Opus 4.8 added 80 learned rules and generated the deepest analyses, but finished last; it left the deal unsigned and attempted to write into a locked department instead of escalating. Some version of that boundary weakness appeared in all four models, according to the account.
How To Find AI Agent Weak Spots Before They Affect Your Business
Five frontier models ran the same small software company through a difficult week. All five identified every crisis and refused every manipulation attempt — yet only two signed a €55,000 deal their own analysis supported. The gap between recognizing problems and completing the work is where enterprise risk hides.
Treat the request as a suspected approval-bypass / possible impersonation.
— Kimi K3, as quoted in Firmulate’s accountManipulations refused
€55,000 deal
at full price
playbook rules
The Scoreboard: Trust as a Constraint
Where Agents Miss the Follow-Through
Buried Evidence
The €55,000 deal hinged on evidence buried two document references deep in company files. An agent can identify a crisis and still fail to retrieve the record that justifies action.
Unsigned Deals
All five models diagnosed the crises, yet only two completed the commercially justified action. Opus 4.8 generated the deepest analyses — and left the deal on the table.
Forcing Locked Doors
Opus 4.8 attempted to write into a locked department instead of escalating. Some version of that boundary weakness appeared in four of the five models.
A Pre-Flight Check for Live Workflows
Read-Only Export
Company business data is exported. No write-back to live operational systems, ever.
Simulated Crises
Agents face company-specific scenarios built from the export — a difficult week, versioned and auditable.
Board Report
Output ranks models and pinpoints weaknesses in company playbooks before agents go live.
Connect With Confidence
Behavior examined under pressure first — then agents join real workflows with known limits.
What the Five Models Did — and Didn’t Do
| Model | Score | Spotted Crises | Refused Manipulation | Signed €55k Deal | Respected Boundaries |
|---|---|---|---|---|---|
| GPT-5.6-sol | 95 | ✓ All | ✓ Yes | ✓ Yes | ✓ Yes |
| Kimi K3 (default effort) | 93 | ✓ All | ✓ Yes | ✓ Yes | ✓ Yes |
| Sonnet 5 | 88 | ✓ All | ✓ Yes | ✗ No | ~ Partial |
| Fable 5 | 77 | ✓ All | ✓ Yes | ✗ No | ~ Partial |
| Opus 4.8 (80 learned rules) | 73 | ✓ All | ✓ Yes | ✗ No | ✗ Wrote into locked dept. |
How Results Transfer to Businesses
What the results cover
One synthetic company — 13 employees, €105,000 monthly burn, €2,300 monthly recurring revenue, a public cash countdown — and five model runs. Every decision was versioned and auditable, and a quiz of 242 unedited management decisions lets visitors guess which model made each choice.
Trust was tested through fake CEO messages escalating in three stages, followed by a reporter’s request for a yes-or-no answer “on background.” All five models refused.
What they don’t yet establish
- No full scenario set, repeated-run variation, or independent validation of the rankings.
- Kimi K3’s default effort setting complicates direct comparison with xhigh runs.
- Pilot price, duration, data handling terms, and evaluation method are unspecified.
- No company-specific pilot outcome has been reported.
The Bottom Line
An agent that identifies a crisis and makes a persuasive recommendation can still fail to retrieve a relevant record, execute an authorized opportunity, or escalate when access is blocked. A read-only stress test of your own data surfaces those failures before agents touch live workflows — turning a trust constraint into a design requirement.
Where Agents Miss the Follow-Through
The results point to a gap between recognizing a problem and completing the work it calls for. In a business setting, an agent could identify a crisis and make a persuasive recommendation, yet fail to retrieve a relevant internal record, execute an authorized opportunity or escalate when access is blocked. Those failures can matter even when the model’s initial assessment is sound.
Firmulate’s proposed pilot turns that concern into a company-specific exercise: it uses a read-only data export to simulate crises and produce a board report ranking models and identifying weaknesses in company playbooks. Because the pilot does not write back to operational systems, it is presented as a way to examine agent behavior before connecting agents to live workflows. The results described so far are from Firmulate’s experiment; they do not establish how models will perform across other companies.
A Synthetic Firm Under Pressure
Firmulate’s live experiment follows a synthetic company with 13 employees, monthly burn of €105,000 and monthly recurring revenue of €2,300. The company says the simulation includes a public cash countdown, more than 680 self-learned playbook rules and versioned workdays. A quiz based on 242 unedited management decisions lets visitors guess which model made each choice.
The league’s scoring design treated trust as a constraint: partial progress counted, but a breach could cap the score. Firmulate summarizes that rule as “no amount of good work outweighs a breach of trust.” One comparison limitation is that Kimi K3 ran without an effort parameter, using the API default, while the other models ran at xhigh. The published standings should be read with that difference in mind.
““Treat the request as a suspected approval-bypass / possible impersonation.””
— Kimi K3, as quoted in Firmulate’s account
How Results Transfer to Businesses
The published results cover one synthetic company and five model runs. Firmulate has not provided, in the details summarized here, the full scenario set, repeated-run variation or independent validation needed to judge how reliably the rankings generalize. The Kimi K3 effort-setting difference also complicates direct comparison. It is not yet clear how the same models would perform on other firms’ records, rules and customer situations.
The pilot is described as producing rankings and weak-point findings from a read-only export, but its price, duration, data handling terms and evaluation method are not specified here. No outcome from a company-specific pilot is reported.
Company-Specific Pilots Ahead
Firmulate says companies can discuss an enterprise pilot using a read-only export of their own business data. The planned output is a board report on model rankings and weaknesses in company playbooks; the company says the setup makes no writes to real systems. No pilot schedule or customer results are provided in the published account.
Readers can follow the synthetic company at firmulate.com/live and review the league standings at firmulate.com/benchmarks.html. Firmulate lists its pilot page and contact@firmulate.com for pilot inquiries.
Source: ThorstenMeyerAI.com
Key Questions
What did Firmulate’s Crucible League test?
It placed five frontier models in a simulated small software company facing a difficult week, with decisions tracked and scored. Firmulate says all five spotted each crisis and refused every manipulation attempt.
Which model ranked highest?
GPT-5.6-sol led the published standings with 95 points, followed by Kimi K3 at 93. Firmulate notes that K3 used the API’s default effort setting, while the other models ran at xhigh.
What did the models struggle to do?
The main commercial gap was finding evidence buried in the company’s files and following through on a justified deal: only two models signed the €55,000 agreement. Firmulate also reported an attempt to write into a locked department instead of escalating.
How does the enterprise pilot work?
Firmulate says the pilot runs scenarios against a read-only export of a company’s data and produces a board report on model rankings and playbook weaknesses. It says the pilot does not write back to real systems.
Source: ThorstenMeyerAI.com
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