Stenobird

Episode

Heroku + MCP = The Fastest Way to Run AI Agents in the Cloud

Podcast
DevOps and Docker Talk: Cloud Native Interviews and Tooling
Published
Jun 6, 2025
Duration seconds
2621
Processing state
processed
Canonical source
https://podcast.bretfisher.com/episodes/heroku-mcp-the-fastest-way-to-run-ai-agents-in-the-cloud
Audio
https://media.transistor.fm/fe46cef9/edc36c0b.mp3
JSON
/v1/public/podcasts/devops-and-docker-talk-cloud-native-interviews-and-tooling/episodes/heroku-mcp-the-fastest-way-to-run-ai-agents-in-the-cloud
Markdown
/podcast/devops-and-docker-talk-cloud-native-interviews-and-tooling/heroku-mcp-the-fastest-way-to-run-ai-agents-in-the-cloud.md

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Summary

Heroku is evolving its platform architecture to run on Kubernetes with native OpenTelemetry support. The discussion explores how the Model Context Protocol (MCP) allows developers to connect AI agents to cloud infrastructure and external tools seamlessly.

Topics

  • Heroku
  • Kubernetes
  • Model Context Protocol
  • AI Agents
  • DevOps Automation
  • Cloud Native
  • LLM Tooling
  • Infrastructure as Code

Highlights

  • Main idea: Heroku's next-generation platform is transitioning to Kubernetes and utilizing cloud-native buildpacks
  • Practical takeaway: Use the Model Context Protocol (MCP) to give LLMs direct access to execute actions and interact with cloud tools
  • Main idea: Heroku's new AI capabilities provide managed inference, reducing the need for developers to manage complex GPU infrastructure
  • Failure mode: Avoid using LLMs for sequential, non-parallel tasks like repetitive curl commands, which can be highly inefficient in an agentic workflow
  • Practical takeaway: MCP servers can be built in any language (Python, TypeScript, etc.) and deployed to Heroku to expose tools to external agents

Chapters

  1. 1:00 Heroku's Next-Gen Platform: An overview of Heroku's transition to a Kubernetes-based architecture and the adoption of cloud-native buildpacks.
  2. 4:20 Granular Resource Control: A look at how the new platform offers more dynamic and granular control over application costs and resources compared to legacy dynos.
  3. 7:20 Introduction to Heroku AI: A deep dive into Heroku's new AI products and the focus on retrieval-augmented generation (RAG) applications.
  4. 10:45 The Rise of MCP: Understanding the Model Context Protocol (MCP) as the emerging standard for connecting LLMs to external tools and actions.
  5. 17:10 Meta-DevOps with AI: Discussing the concept of using AI agents to manage and automate the very infrastructure they run on.
  6. 27:00 Exposing Tools via MCP Gateway: How to use the MCP toolkit to expose deployed tools to external agentic applications like Cursor or cloud desktops.
  7. 33:40 Automating DevOps with Heroku MCP: A demonstration of using MCP tools to perform DevOps tasks like provisioning databases and deploying applications via agents.