Topic
16 facts from 1 episode, in date order. These are what speakers said on the shows, each linked to the moment it was said. They aren't independently verified.
2026
Bar Kogan estimates that over 50 percent of agent use in a typical enterprise is autonomous coding agents and assistants, and about 45 percent is low-code automations.
“over 50 is the autonomous, uh, coding agents and assistants in the average enterprise. Then probably 45, uh, is, uh, is those, uh, uh, low code automations.”
2026
Bar Kogan describes Onyx Security as a two-year-old company that nonetheless attracted inbound interest from Fortune 10 and 20 customers.
“We're a two year old company, we're like a few people from, uh, you know, Pudan Math and Cyber.”
2026
Bar Kogan says the two primitives of 2026 AI, large language model foundation models and LLM-driven tool calling agents, have not changed much in recent years, helping Onyx keep its core technology focused.
“we're still building agents in pretty much the same way, where we have an LLM decide what are the tool calls that we're gonna make and generate those.”
Undated
Onyx Security trains models and builds agents that oversee other AI agents.
“Number one is we train models and build agents that can oversee other agents.”
Undated
Onyx Security sells its agent-oversight capability as a product called the secure AI control plane.
“we basically productize it in a product that we call the AI control plane or the secure AI control plane.”
Undated
Bar Kogan says limiting a software system's permissions is traditionally the first and most important security control, but autonomous agents are given a user's broad permissions.
“Because, and then, no matter what, even if it goes wrong, even if it's compromised, it can't, um, typically do any stuff that it was originally allowed to do.”
Undated
Bar Kogan argues that existing endpoint and API security tools lack the context to understand what flexible AI agents are doing.
“So a lot of these existing tools, they don't have the context to understand what these very flexible, unpredictable systems are doing.”
Undated
Bar Kogan says running a frontier agent to supervise every agent would cost customers more than the AI they are overseeing and would add too much delay.
“if I need to run an agent for every agent you're running as your security vendor, uh, you're gonna be paying for me more than you're paying for your AI, right?”
Undated
Onyx trains small models whose single job is to decide when a smarter agent should review an action, keeping cost and latency low.
“They're very small, they're, they almost can't do anything else other than be able to say, should I have a smarter agent? Look at this.”
Undated
Bar Kogan says most of Onyx's research and engineering staff come from an Israeli intelligence unit focused on math and cyber.
“Most of the people in our company, most of our research engineering, come from a unit in the Israeli intelligence, where we actually deal with math and cyber and the intersection thereof.”
Undated
Sarah Guo says Onyx co-founder Gil previously built synthetic data and worked at Nvidia.
“Your co founder Gil came out of building synthetic data and working at Nvidia.”
Undated
Bar Kogan argues that if AI companies become ten-trillion-dollar firms, customers will want a non-vendor company overseeing AI, which he calls a market worth over 100 billion dollars.
“If you have AI companies that are 10 trillion dollar companies, we think you want a company that is not the vendor of the AI itself to oversee and help you control what the AI is doing.”
Undated
Bar Kogan argues security buyers prefer an independent party over the vendor of a product to certify it is safe.
“you're not gonna trust the vendor of a product to tell you that this product is not gonna mess your environment.”
Undated
Bar Kogan says enterprises are unwilling to share historical agent behavior data with Anthropic or OpenAI because they fear it would be used for training, which gives Onyx a data advantage.
“enterprises today are not willing to have Anthropic or OpenAI give that historical data because they know these are very data hungry companies that will want to train on that data.”
Undated
Bar Kogan says models increasingly make errors from independent, semi-aware perspectives that may not align with the user's, and that this grows with model intelligence.
“Maybe just the the way it is that as you get smarter, you have more independent thoughts and and more, uh, you're more conscious”
Undated
Bar Kogan says Onyx believes mechanistic interpretability, studying a model's internal weights and activations, will be at least part of controlling advanced AI.
“We believe that understanding the internal weights and activations, what is the internal structure, the mathematical structure of these systems, is gonna be at least part of the solution.”
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