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In this episode of the Modern Web Podcast, Rob Ocel and Danny Thompson sit down with Julián Duque, Principal Developer Advocate at Heroku, to talk about Heroku’s evolution into an AI Platform-as-a-Service. Julián breaks down Heroku’s new Managed Inference and Agents (MIA) platform, how they’re supporting Claude, Cohere, and Stable Diffusion, and what makes their developer experience stand out.
They also get into Model Context Protocols (MCPs)—what they are, why they matter, and how they’re quickly becoming the USB-C for AI. From internal tooling to agentic infrastructure and secure AI deployments, this episode explores how MCPs, trusted environments, and better AI dev tools are reshaping how we build modern software.
Key Points from this episode:
- Heroku is evolving into an AI Platform-as-a-Service with its new MIA (Managed Inference and Agents) platform, supporting models like Claude, Cohere, and Stable Diffusion while maintaining a strong developer experience.
- MCPs (Model Context Protocols) are becoming a key standard for extending AI capabilities—offering a structured, secure way for LLMs to access tools, run code, and interact with resources.
- Heroku's AI agents can perform advanced operations like scaling dynos, analyzing logs, and self-healing failed deployments using grounded MCP integrations tied to the Heroku CLI.
- Despite rapid adoption, MCPs still have rough edges—developer experience, tooling, and security protocols are actively improving, and a centralized registry for MCPs is seen as a missing piece.
Chapters
0:00 – What is MCP and why it matters
3:00 – Heroku’s pivot to AI Platform-as-a-Service
6:45 – Agentic apps, model hosting, and tool execution
10:50 – Why REST isn’t ideal for LLMs
14:10 – Developer experience challenges with MCP
18:00 – Hosting secure MCPs on Heroku
23:00 – Real-world use cases: scaling, healing, recommendations
30:00 – Common scaling challenges and hallucination risks
34:30 – Testing, security, and architecture tips
36:00 – Where to start and final advice on using AI tools effectively
Follow Julián Duque on Social MediaTwitter/X: https://x.com/julian_duque
Linkedin: https://www.linkedin.com/in/juliandavidduque/
Sponsored by This Dot: thisdotlabs.com
4.4
1212 ratings
In this episode of the Modern Web Podcast, Rob Ocel and Danny Thompson sit down with Julián Duque, Principal Developer Advocate at Heroku, to talk about Heroku’s evolution into an AI Platform-as-a-Service. Julián breaks down Heroku’s new Managed Inference and Agents (MIA) platform, how they’re supporting Claude, Cohere, and Stable Diffusion, and what makes their developer experience stand out.
They also get into Model Context Protocols (MCPs)—what they are, why they matter, and how they’re quickly becoming the USB-C for AI. From internal tooling to agentic infrastructure and secure AI deployments, this episode explores how MCPs, trusted environments, and better AI dev tools are reshaping how we build modern software.
Key Points from this episode:
- Heroku is evolving into an AI Platform-as-a-Service with its new MIA (Managed Inference and Agents) platform, supporting models like Claude, Cohere, and Stable Diffusion while maintaining a strong developer experience.
- MCPs (Model Context Protocols) are becoming a key standard for extending AI capabilities—offering a structured, secure way for LLMs to access tools, run code, and interact with resources.
- Heroku's AI agents can perform advanced operations like scaling dynos, analyzing logs, and self-healing failed deployments using grounded MCP integrations tied to the Heroku CLI.
- Despite rapid adoption, MCPs still have rough edges—developer experience, tooling, and security protocols are actively improving, and a centralized registry for MCPs is seen as a missing piece.
Chapters
0:00 – What is MCP and why it matters
3:00 – Heroku’s pivot to AI Platform-as-a-Service
6:45 – Agentic apps, model hosting, and tool execution
10:50 – Why REST isn’t ideal for LLMs
14:10 – Developer experience challenges with MCP
18:00 – Hosting secure MCPs on Heroku
23:00 – Real-world use cases: scaling, healing, recommendations
30:00 – Common scaling challenges and hallucination risks
34:30 – Testing, security, and architecture tips
36:00 – Where to start and final advice on using AI tools effectively
Follow Julián Duque on Social MediaTwitter/X: https://x.com/julian_duque
Linkedin: https://www.linkedin.com/in/juliandavidduque/
Sponsored by This Dot: thisdotlabs.com
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