Show notes
Summary In this episode Craig McLuckie, co-creator of Kubernetes and founder/CEO of Stacklok, talks about how to improve security and reliability for AI agents using curated, optimized deployments of the Model Context Protocol (MCP). Craig explains why MCP is emerging as the API layer for AI‑native applications, how to balance short‑term productivity with long‑term platform thinking, and why great tools plus frontier models still drive the best outcomes. He digs into common adoption pitfalls (tool pollution, insecure NPX installs, scattered credentials), the necessity of continuous evals for stochastic systems, and the shift from “what the agent can access” to “what the agent knows.” Craig also shares how ToolHive approaches secure runtimes, a virtual MCP gateway with semantic search, orchestration and transactional semantics, a registry for organizational tooling, and a console for self‑service—along with pragmatic patterns for auth, policy, and observability. 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Build end-to-end AI workflows that integrate seamlessly with your existing tech stack. Join the ranks of forward-thinking organizations that are revolutionizing their data engineering with Bruin. Get started today at aiengineeringpodcast.com/bruin, and for dbt Cloud customers, enjoy a $1,000 credit to migrate to Bruin Cloud.Your host is Tobias Macey and today I'm interviewing Craig McLuckie about improving the security of your AI agents through curated and optimized MCP deploymentInterviewIntroductionHow did you get involved in machine learning?MCP saw huge growth in attention and adoption over the course of this year. What are the stumbling blocks that teams run into when going to production with MCP servers?How do improperly managed MCP servers contribute to security problems in an agent-driven software development workflow?What are some of the problematic practices or shortcuts that you are seeing teams implement when running MCP services for their developers?What are the benefits of a curated and opinionated MCP service as shared infrastructure for an engineering team?You are building ToolHive as a system for managing and securing MCP services as a platform component. What are the strategic benefits of starting with that as the foundation for your company?There are several services for managing MCP server deployment and access control. What are the unique elements of ToolHive that make it worth adopting?For software-focused agentic AI, the approach of Claude Code etc. to be command-line based opens the door for an effectively unbounded set of tools. What are the benefits of MCP over arbitrary CLI execution in that context?What are the most interesting, innovative, or unexpected ways that you have seen ToolHive/MCP used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on ToolHive?When is ToolHive the wrong choice?What do you have planned for the future of ToolHive/Stacklok?Contact InfoGitHubLinkedInParting QuestionFrom your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?LinksStackLokMCP == Model Context ProtocolKubernetesCNCF == Cloud Native Computing FoundationSDLC == Software Development Life CycleThe Bitter LessonTLA+Jepsen TestsToolHiveAPI GatewayGleanThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0