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Building AI Agents on the Frontend with Sam Bhagwat and Abhi Aiyer

57 min episode · 2 min read
·
Sam Bhagwat,Building Ai Agents

Episode

57 min

Read time

2 min

Topics

Productivity, Artificial Intelligence, Software Development

AI-Generated Summary

Key Takeaways

  • TypeScript-first architecture: Maastra targets JavaScript developers by providing AI agent primitives in TypeScript rather than Python, eliminating language switching overhead and enabling full-stack development with consistent tooling across frontend and backend components.
  • Agent complexity spectrum: Agentic behavior exists on a spectrum from single LLM calls with tools to multi-agent networks. Agents struggle with more than 8-10 tools simultaneously, requiring workflow orchestration to group tools into manageable categories for reliable execution.
  • Workflow determinism pattern: Engineers often know the exact steps needed for complex tasks. Maastra workflows let developers write deterministic code paths while using LLMs only at specific execution points, avoiding unreliable model-driven orchestration for predictable processes.
  • MCP integration strategy: Model Context Protocol servers function as decentralized integration hubs, allowing Maastra agents to access third-party tools written in any language. This eliminates manual integration work while expanding agent capabilities through community-built MCP servers.

What It Covers

Sam Bhagwat and Abhi Aiyer discuss Maastra, an open-source TypeScript framework for building AI agents with primitives like workflows, tools, and RAG, addressing the gap in frontend-focused AI development tooling.

Key Questions Answered

  • TypeScript-first architecture: Maastra targets JavaScript developers by providing AI agent primitives in TypeScript rather than Python, eliminating language switching overhead and enabling full-stack development with consistent tooling across frontend and backend components.
  • Agent complexity spectrum: Agentic behavior exists on a spectrum from single LLM calls with tools to multi-agent networks. Agents struggle with more than 8-10 tools simultaneously, requiring workflow orchestration to group tools into manageable categories for reliable execution.
  • Workflow determinism pattern: Engineers often know the exact steps needed for complex tasks. Maastra workflows let developers write deterministic code paths while using LLMs only at specific execution points, avoiding unreliable model-driven orchestration for predictable processes.
  • MCP integration strategy: Model Context Protocol servers function as decentralized integration hubs, allowing Maastra agents to access third-party tools written in any language. This eliminates manual integration work while expanding agent capabilities through community-built MCP servers.

Notable Moment

The team initially built Maastra with a GUI-first approach in October 2024. After respected developers responded with polite disinterest, they completely rebuilt it as code-first within weeks, keeping only the playground visualization component.

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Episode Transcript

Most AI agent frameworks are back end focused and written in Python, which introduces complexity when building full stack AI applications with JavaScript or TypeScript front ends. This gap makes it harder for front end developers to prototype, integrate, and iterate on AI powered features. Maastra is an open source TypeScript framework focused on building AI agents and has primitives such as agents, tools, workflows, and RAG. Sam Bhagwat and Abhi Iyer are cofounders at Maastra. They join the podcast with Nick Nisi to talk about this state of front end tooling for AI agents, AI agent primitives, MCP integration, and more. Nick Niese is a conference organizer, speaker, and developer focused on tools across the web ecosystem. He has organized and emceed several conferences and has led Nebraska JS for more than a decade. Nick currently works as a developer experience engineer at WorkOS. Welcome to Software Engineering Daily. Today's episode features the founders of Mastro, and I'd like to welcome to the show Sam Bhagwat and Abhi Iyer. How's it going, guys? Hey. Great to be here. Great to have you. Sam, why don't you go first and introduce yourself? So probably I'm best known for being the cofounder of Gatsby, and I worked at a few startups around the valley before that as an engineer. But I'm also the author of a book called Principles of Building AI Agents, and now the cofounder and CEO of Maastra. Nice. Well, welcome to the show. Happy to have you. I've definitely heard of Gatsby. Abi, why don't you introduce yourself? Oh, yeah. I'm Abi Iyer. I'm the CTO and, cofounder of Maastra. We have also the third cofounder. He's not here. His name is Shane. Just wanna put the poor one out for him. Yeah. And I used to work at Gatsby as well very early on, and that's how Sam and I met. And then I also worked at Netlify because we got acquired by them. I'm sure we'll get into that, but that's how I got here. Nice. So, yeah, just digging right in. How did you come together on Mastro? Was it something that was born out of something that you worked on Gatsby? Or yeah. I'm just curious how it got started. Probably the best way of saying it is that it was born out of frustration. So after Gatsby and sort of, like, we left Netlify at at various points, but we were working in building a CRM. And we kinda thought that, like we thought we did a lot of fun building dev tools and we're gonna build something new. But it actually turned out that as we were building the AI parts of that CRM, which we called Kepler and we were building this CRM for both of us together for about, like, nine months or so. We started using building the AI parts of Kepler and using the kind of TypeScript AI libraries that were available at the time. And it …

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  • Sam Bhagwat and Abhi Aiyer discuss Maastra, an open-source TypeScript framework for building AI agents with primitives like workflows, tools, and RAG, addressing the gap in frontend-focused AI development tooling.
  • Model Context Protocol servers function as decentralized integration hubs, allowing Maastra agents to access third-party tools written in any language.

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