Massive Signals

Vrajesh Bhavsar: Securing the Agentic Stack

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In this episode, we sit down with Vrajesh Bhavsar, founder and CEO of Operant AI, a platform built to protect AI agents, cloud workloads, and MCP ecosystems at runtime. Vrajesh traces his path from Apple’s Secure Enclave to Arm’s ML business unit to the founding bet that security couldn’t scale on dashboards and human intervention alone. We cover how early design partners forced Operant’s focus, what it cost to break into cyber as an outsider, and how a Kubernetes-native foundation became the right architecture for a world that didn’t exist yet. We also get into the pricing model they figured out before the SaaS world caught up, what it meant when seven people showed up to the first enterprise demo call, and why agentic identity is the most overhyped category in AI security right now.



In this episode:

 

  • The Early Vision Problem: Operant started broad — it took real customer feedback to force the security focus that made everything else possible.
  • The Outsider Tax: Breaking into cyber without a Palo Alto or Zscaler pedigree was hard, and Vrajesh doesn’t pretend otherwise.
  • The Kubernetes Bet: Most enterprises weren’t sure Kubernetes would matter. Operant built their entire foundation on it anyway.
  • The Pricing Call: They built utility-based pricing before seat-based SaaS came under pressure — and the market caught up to them.
  • Seven People on the First Call: When enterprises started showing up to demos with full teams and no foot-dragging, the inflection point had arrived.



    • Connect more with Vrajesh Bhavsar | Operant AI

     

    Website: https://www.operant.ai 

    LinkedIn: https://www.linkedin.com/in/vrajeshio 



    Connect more with Massive Signals

     

    Website: https://massive.vc/ 

    LinkedIn: https://www.linkedin.com/in/arinewman 

    LinkedIn: https://www.linkedin.com/in/davidgmandell

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