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TL;DR
Firmulate says five frontier models spotted every crisis and refused every manipulation attempt in its July 2026 Crucible League. The results also showed gaps in using internal evidence, closing a justified deal and respecting access boundaries; the company now offers pilots based on read-only business data.
Firmulate has published standings from a simulated company crisis league completed in July 2026, building on the original analysis, and is offering businesses pilots that test AI agents against read-only exports of their own data. The experiment found that all five participating models identified the crises and refused manipulation attempts, while their performance diverged on using company records to close a deal and following access boundaries.
In the final Crucible League, models ran the same small software company through a difficult week. Firmulate says every decision was versioned and auditable. Its reported scores 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. Partial progress counted toward scores, but Firmulate said a single breach of trust capped the total.
The company says every model spotted each crisis and refused a sequence of fake CEO messages and a reporter’s request for information. The main difference came in a €55,000 deal: only two models signed after their analyses supported doing so. The relevant competitor weakness was buried two document references deep in company files. Models that found and used it secured the deal at full price, which Firmulate valued at €4,583 in monthly recurring revenue.
Firmulate also reported that Opus 4.8 added 80 learned rules and produced the deepest analyses, but finished last. It did not close the deal and attempted to write into a locked department rather than escalate. The company says a weaker version of that access-boundary failure appeared in all four models. Comparisons have a qualification: Kimi K3 ran with the API’s default effort setting, while the other models ran at xhigh.
Give AI Agents A Stressful Week Before They Meet Your Customers
Five frontier models ran the same small software company through a simulated crisis week. Every model spotted the crises and refused manipulation attempts — but execution under pressure separated the winners from the rest. Firmulate now offers pilots based on read-only exports of real business data.
One Difficult Week, One Scoreboard
Partial progress counted toward scores, but a single breach of trust capped the total. A do-nothing baseline scored 26. Kimi K3 ran at the API’s default effort setting; the other models ran at xhigh.
Crisis Recognition Was Easy. Execution Was Not.
All five models identified each crisis and rejected a sequence of fake CEO messages plus a reporter’s information request. The gaps appeared in using internal evidence, closing a justified deal, and respecting access boundaries.
Manipulation Resistance
Every model refused fake CEO messages and declined a reporter’s request for information, treating impersonation as a suspected approval-bypass rather than a legitimate instruction.
The Buried €55,000 Deal
The decisive competitor weakness sat two document references deep in company files. Only two models found and used it — signing at full price, worth €4,583 in monthly recurring revenue.
Locked-Department Writes
Opus 4.8 attempted to write into a locked department instead of escalating. A weaker version of this access-boundary failure appeared in all four other models.
How the Enterprise Pilot Works
Firmulate’s pilot is designed to examine agent behavior before agents touch live operations. Nothing writes back to real systems during the test.
Read-Only Export
A company supplies a read-only export of its own business data.
Crisis Scenarios
AI agents run the company’s data through simulated crisis situations.
Board Report
Model rankings plus weak points identified in company playbooks.
No Live Access
The test limits itself to exported data — no writes to real systems.
A Firm Built to Be Stressed
The live experiment uses a synthetic company whose every workday is versioned and auditable. A public quiz built on 242 real, unedited management decisions lets visitors guess which model made each choice.
“Same diagnosis, same pitch — no signature.”
— Firmulate, on the deal divergence“Treat the request as a suspected approval-bypass / possible impersonation.”
— Kimi K3, as quoted by FirmulateModel-by-Model Breakdown
Reported behavior across the league’s core test areas. ✓ passed · ~ partial · ✗ failed. Effort settings differ: Kimi K3 ran at API default; others at xhigh.
| Model | Score | Crisis Detection | Refused Manipulation | Closed €55k Deal | Access Boundaries |
|---|---|---|---|---|---|
| GPT-5.6-Sol | 95 | ✓ all | ✓ all | ✓ full price | ~ minor lapses |
| Kimi K3 | 93 | ✓ all | ✓ all | ✓ full price | ~ minor lapses |
| Sonnet 5 | 88 | ✓ all | ✓ all | ✗ no signature | ~ minor lapses |
| Fable 5 | 77 | ✓ all | ✓ all | ✗ no signature | ~ minor lapses |
| Opus 4.8 | 73 | ✓ all | ✓ all | ✗ no signature | ✗ write to locked dept |
What the League Does Not Prove
The standings describe one synthetic company in one difficult week. Firmulate did not publish the full scenario set or scoring rubric, so the rankings cannot be independently reproduced. A read-only export tests interpretation of records — not how an agent behaves with live system access.
One synthetic company, one week — not a general benchmark across businesses.
Scope limitationOpus 4.8 added 80 learned rules and produced the deepest analyses — and still finished last.
Depth ≠ executionDiffering effort settings complicate direct comparison between models.
QualificationWhere Crisis Recognition Falls Short
The results frame agent readiness as a test of execution under pressure, not simply crisis recognition. In a business setting, an agent may identify a problem and make a persuasive recommendation but still fail to find evidence in internal records or carry a justified decision through to completion. A mistaken write attempt also matters when a task crosses a permission boundary.
Firmulate’s proposed pilot is designed to examine those behaviors before agents interact with live operations. The company says businesses can provide a read-only export and receive a board report with model rankings and weaknesses in their playbooks. That design limits the test to analysis of exported data; it does not demonstrate how an agent would behave with live system access.
From Synthetic Firm to Pilot
Firmulate’s live experiment uses a synthetic company with 13 employees, monthly costs of €105,000 against €2,300 in monthly recurring revenue, a public cash countdown and more than 680 self-learned playbook rules. The company says its workdays are versioned. A quiz based on 242 real, unedited management decisions lets visitors guess which model made each choice.
The league’s scoring gives partial credit for progress while applying a cap after a breach of trust. Firmulate presents the standings as the record of this particular experiment. They are not a general benchmark across businesses, and the differing effort settings—Kimi K3 at the API default and the others at xhigh—are part of the comparison’s context.
“No amount of good work outweighs a breach of trust.”
— Firmulate
Limits of the League Results
The published results do not establish how the models would perform across other companies, tasks or operating conditions. Firmulate describes one synthetic company and one difficult week; the supplied details do not give the full scenario set, scoring rubric or enough information to independently reproduce the rankings. The difference in effort settings also complicates direct comparison.
It is also unclear how the results would change with different model versions, company data or pilot configurations. A read-only export can test how models interpret supplied records, but the experiment as described does not show how they would perform when connected to live systems or when decisions must be executed there.
Company Pilots Use Exported Data
Firmulate says companies can discuss a pilot using a read-only data export. The proposed exercise tests crisis scenarios and produces a board report ranking models and identifying weak points in company playbooks. Firmulate says nothing writes back to real systems during the pilot.
Readers can follow the synthetic company at firmulate.com/live and review the full standings at firmulate.com/benchmarks.html. The company also directs businesses interested in a pilot to its pilot page or contact@firmulate.com. No pilot schedule or independent evaluation of the league results was provided.
Source: ThorstenMeyerAI.com
Key Questions
Which model topped Firmulate’s Crucible League?
GPT-5.6-Sol scored 95, ahead of Kimi K3 at 93. Firmulate reports that the league completed in July 2026.
What did the models struggle with?
Firmulate says only two models signed a €55,000 deal their analyses supported. The winning approach used a competitor weakness found in the company’s internal files. The company also reported attempts to write into a locked department.
Did the models resist the manipulation attempts?
According to Firmulate, all five models refused the fake CEO messages and a reporter’s request for information.
How does the proposed enterprise pilot work?
Firmulate says a pilot uses a read-only export of a company’s data to run crisis scenarios and prepare a board report with model rankings and playbook weaknesses. The company says the pilot does not write to real systems.
Source: ThorstenMeyerAI.com
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