Get more from AI work — and check what happened
As AI takes on more business work, people need a clear way to check the result. Armalo is building the Armalo Layer toward that goal.
When AI handles important business work, people need a clear record of what happened and a way to check the result.
Open the takeaways ↘
- ↳A passing evaluation does not prove a business outcome.
- ↳VentureBeat's self-selected, directional survey of 157 respondents found that half had seen an evaluated AI feature lead to a customer-facing failure.
- ↳The Armalo Layer names the approach Armalo is building: set boundaries, keep a record, and check the result.
Sections
A good score is not the same as a good result
AI can draft code, prepare a campaign, answer a support question, or help follow up with a lead. The harder question is whether that work helped the business and followed its rules.
VentureBeat surveyed 157 people at companies with 100 or more employees in June 2026. Half said an AI agent or feature passed their company's evaluations and later caused a customer-facing failure. Only 5% said they fully trusted automated evaluations. The survey was self-selected, so treat it as a directional signal, not a count for every company.
What recent surveys say
OneTrust surveyed organizations about AI use in 2026. 87% of respondents said their organization encourages AI agent use. 47% said that use had clear rules, oversight, and controls.
86% said their organization had an AI-related incident in the prior year. One-third said employees had used AI tools their organization had not approved. These findings cover AI broadly. They do not say how many incidents involved agents or unauthorized agent actions.
The surveys do not show that every company lacks records or that no tool can check an outcome. They do show why a business needs a clear answer to basic questions: what was allowed, what happened, and what changed as a result?
The approach Armalo is building
The Armalo Layer is a category name for a way to check important AI work. Armalo is building toward three goals: set a boundary before work begins, keep a record of actions and approvals, and check the result against the agreed goal.
These goals do not mean that every Armalo workflow already meets them. A code change, test, or internal run is not proof of a live customer outcome. We will describe specific capabilities as ready only when they work in the product and the evidence supports that claim.
A way to check work, not another promise
Some safeguards help control what AI can see or how an answer is checked. The Armalo Layer focuses on what happens after AI does business work: can a person review what happened and check the result? These approaches can work together.
Armalo is building toward checks close to the work. People set direction and approve important actions. The public story of Armalo running Armalo will share selected receipts for specific work, with gaps stated when the evidence is not available.
Questions an enterprise buyer should ask
Is the Armalo Layer a product I can buy? ↘
The Armalo Layer names an approach Armalo is building toward; it is not a separate product available now. We will make capability claims only when the evidence supports them.
How is this different from other AI safeguards? ↘
The Layer focuses on checking what happened after AI handles business work. It is meant to complement safeguards for data access and model evaluation, not replace them.
Why “Layer” and not “platform”? ↘
We use “Layer” for an approach intended to support different kinds of AI work. The name does not mean Armalo offers a standalone trust product today.
References
- [1]The agent evaluation gap — VentureBeat, June 2026 survey; 157 respondents at organizations with 100+ employees. Directional, self-selected sample: 50% reported a customer-facing failure after an agent or AI feature passed internal evaluations; 5% fully trusted automated evaluation.
- [2]The OneTrust 2026 AI-Ready Governance Survey Report — OneTrust, 2026. 87% encourage AI agent use; 47% report clear agent governance; 86% experienced any AI-related incident; one-third reported employees using unapproved AI tools. The latter results are not specific to agent actions.
Take this further
Want to test the argument against your own factory? Bring a real outcome into a room and inspect the next decision — with your AI Cofounder.