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The enterprise AI industry has a math problem. Cisco data shows 85% of enterprises are piloting AI agents, but only 5% have shipped them to production. At VB Transform 2026 on Tuesday, Bryan Silverthorn, Director of AGI Autonomy at Amazon, explained why that gap persists — and why the answer isn’t better benchmarks.
Silverthorn, who joined Amazon through its acquisition of Adept AI and now leads multimodal agent training inside the company’s AGI lab, argued that reliability must be broken into four distinct dimensions: consistency, robustness, predictability, and safety — a framework he credits to research from Princeton.
“It unpacks different factors that I see tangled together in almost every eval I’ve ever seen,” he said.
The framework matters because agents routinely ace internal evaluations and then collapse in the wild. Silverthorn described a customer that deployed an agent for software QA involving serial number extraction from screens. It worked flawlessly for two months — then began intermittently reading wrong numbers. The culprit: the underlying vision encoder behaved differently depending on where the serial number appeared on screen, and a software change imperceptible to humans triggered the failure.
The lesson, Silverthorn said, is about measurement, not just models. “The models have to be better. Obviously, we’re working hard on making the models better,” he said. But the deeper takeaway, he added, is that teams need to identify their dimensions of variability and match measurement rigor to the stakes of the application. VentureBeat’s own proprietary research, presented before the session, reinforces the point: half of surveyed companies shipped agents that passed internal evals but failed real customers, and enterprises overwhelmingly track uptime while ignoring accuracy — checking the pulse without checking the diagnosis. A related finding underscored how few guardrails exist: most enterprises default to the model makers’ own evaluations and little else, leaving their testing strategy, as I described it on stage, a coin flip between trusting the vendor and trusting nothing.
Silverthorn’s most memorable prescription was cultural, not technical. Inside Amazon’s AGI lab, researchers literally call their agents “interns” — as in, “I’ll have my intern talk to your intern.” The joke carries a serious operational philosophy. Agents, like interns, are powerful but occasionally clueless, capable of amazing work and spectacular derailment.
Managing them, he argued, requires management skills rather than software skills: asking what could go wrong, adding backups and undo capabilities, and consciously deciding what risk you can accept. “You can ask the intern, ‘Hey, what might you do wrong here? How might you mitigate your negative outcomes?'” he said. Amazon’s lab has embraced that trade-off, accepting agents occasionally running the wrong experiment in exchange for research velocity — including one agent running experiments around the clock on its own high-level research plan.
Silverthorn was candid about the limits of today’s technology. Self-improving AI remains “a loaded term,” he said — Amazon uses AI to improve its models constantly, but fully autonomous self-improvement is distant. Computer use remains a core focus of his lab, with a commercial trucking customer already using browser automation to stitch together warranty claims across fragmented systems**, though he stressed that no future agent will rely on computer use alone — it will work alongside MCP, APIs, and other tools to complete end-to-end workflows**. And LLM-as-judge techniques, while promising, are just one of several strategies for aligning agent capability with acceptable risk.
For enterprises stuck in pilot purgatory, the path forward starts with a mindset shift: stop asking whether your agent can do something impressive once, and start asking whether it can do it correctly a thousand times in a row.
In other words, the enterprises that escape the 85% ceiling won’t be the ones with the smartest agents. They’ll be the ones with the best managers.
Hundreds of enterprise leaders and technical experts packed the main ballroom of the luxurious Hotel Nia in Menlo Park this week for VB Transform 2026, the year’s preeminent conference on using generative AI agents to drive business outcomes.
Rachad Alao, vice president of product engineering at the rising Canadian enterprise AI startup Cohere, joined VentureBeat CEO and editor-in-chief Matt Marshall for a fireside chat about building agentic systems without surrendering sensitive data, infrastructure control, or the ability to change vendors.
Alao, who previously led responsible AI and trust and safety engineering teams at Google and Meta, argued that AI sovereignty means more than downloading an open model or running an application behind a corporate firewall.
Asked how Cohere defines sovereignty, Alao pointed to organizations operating mission-critical systems, including banks, hospitals and governments.
“It is important to have very tight control on where the data resides, have tight control on the AI,” he said, adding that AI operations should take place in jurisdictions an organization understands or directly controls.
That extends from GPUs and private-cloud infrastructure through governance systems that route requests among models, as well as the connectors, search tools and agent frameworks acting on enterprise data.
“You want to have control on the entire stack,” Alao said.
Marshall challenged one of the central economic arguments for smaller, locally deployed models: Inference prices continue to fall rapidly, potentially weakening the case for optimizing every token.
Alao countered that total consumption is climbing even faster as enterprises move from relatively simple chatbots to agents that reason through problems, call tools, search internal systems and take multiple steps before returning an answer.
“Your token utilization is going exponentially up, because you’re dealing with more and more complex agentic use cases,” he said. Those workflows require “a lot of processing, thinking, tools interaction” to complete their objectives, he added.
Alao also drew a contrast between providers that bill customers according to token consumption and Cohere’s approach.
“If your whole way of charging customers is for token utilization, you want to maximize token utilization,” he said. “We do not sell our models and our platform that way.”
Instead, Alao said Cohere tries to help enterprises solve their hardest problems privately and securely while reducing unnecessary model usage. His prescription was straightforward: “Use the right model for the task at hand.”
Rather than sending every request to the largest available frontier model, enterprises should route work according to the intelligence required and the sensitivity or regulatory burden attached to the task.
Alao cited an unnamed Canadian bank that uses Cohere’s on-premises models for highly regulated workloads, while sending less sensitive tasks requiring greater intelligence through Cohere’s North platform to larger frontier models.
“So model routing can become super useful,” he said.
Asked by an audience member how Cohere’s open-source North Mini Code, released last month, could compete against proprietary coding models, Alao acknowledged that larger frontier models may perform somewhat better on the hardest tasks.
But that advantage may not justify using them indiscriminately.
“For 80% of the use cases that they needed, this was a lot more effective, a lot cheaper,” Alao said of developers adopting the model.
Cohere’s North Mini Code runs on a single Nvidia H100 GPU and targets agentic software engineering, including terminal work, code review and tool use.
The company has also released Command A+, a 218-billion-parameter mixture-of-experts model with only 25 billion parameters active during each generation step.
Its compressed four-bit version reduces the hardware required for private deployment, while its Apache 2.0 license gives enterprises broad freedom to operate and modify it.
Asked about Cohere’s longstanding work on embeddings and enterprise search, Alao said the field is moving beyond retrieving text and inserting it into a model’s context window.
“Today, the state of the art is around multimodal search,” he said. “It’s beyond just the text modality.”
Search across documents, images and other forms of information is becoming “an integral component of your agentic workflow,” Alao added, with the model deciding when and how to use retrieval like any other tool.
Asked what would persuade enterprises to move beyond bundled AI services from existing cloud providers, Alao returned to data control and portability.
“If you’re interested in sovereignty, you want to have more control on your data,” he said. Cohere’s governance layer, he added, lets customers route traffic to appropriate models, “breaking that vendor lock-in concern that a lot of our customers have.”
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