MCP in Prod: Becoming the Electricity Every AI Agent Needs | Ajay Jumar, Head of MCP at Infotrack

What happens when your customer is an AI agent, not a person? For 25 years InfoTrack has sold authoritative legal and property data to people clicking through interfaces. Now it is making those same services callable by AI agents using the Model Context Protocol (MCP). That shift raises a sharper question: How do you actually run MCP in production inside a regulated enterprise? In this episode of Agents After Dark, https://www.linkedin.com/in/matthewdoughty/ sits down with https://www.linkedin.com/in/ajay-kumar87/, to break down what it takes to move Model Context Protocol from experiment to production. Together they discuss: Why InfoTrack stood up a dedicated MCP business unit instead of treating AI as an experiment How an enterprise MCP gateway works in practice: a central registry, a search-and-invoke pattern, and vectorised tools that cut token costs across hundreds of services Why human-in-the-loop elicitation matters the moment an agent can spend money, and why most major providers still don't support it out of the box Why hallucination is an agent problem, not an MCP problem, since MCP itself is deterministic How MCP security is evolving after incidents like the Aura health breach The shift in customer mindset from "what is MCP?" to "make my agent more productive" Why roughly 62% of companies are experimenting with agents while only 28% are scaling, and what separates the two As agents move from assistants to actors that order, transact, and execute, the businesses that win may be the ones whose products are built to be consumed by software. This conversation is a practical guide to running Model Context Protocol in production, covering architecture, security, and go-to-market, for any enterprise starting its own MCP journey. About Ajay Ajay Kumar is Head of MCP Services at InfoTrack, where he leads the company's Model Context Protocol business unit and shapes how AI moves from experimentation into core production workflows. With over 17 years across banking, prop-tech, insurance, and government, Ajay brings an engineering-first perspective on identity, architecture, and enterprise-scale delivery, and on what it actually takes to make agents and MCP work in regulated environments. About Prefactor https://www.prefactor.ai/ helps enterprises trust AI in production. As organisations deploy more AI agents, maintaining visibility into performance, risk, and operational quality becomes increasingly difficult. Prefactor gives engineering, product, and security teams a single platform to monitor AI systems, evaluate outcomes, identify risks, and take action when things go wrong. Learn more at http://prefactor.ai