A Candid Conversation Around MCP and A2A // Rahul Parundekar and Sam Partee // #316 SF Live

A Candid Conversation Around MCP and A2A // Rahul Parundekar and Sam Partee // #316 SF Live

Author: Demetrios May 21, 2025 Duration: 1:04:42

Demetrios, Sam Partee, and Rahul Parundekar unpack the chaos of AI agent tools and the evolving world of MCP (Model Context Protocol). With sharp insights and plenty of laughs, they dig into tool permissions, security quirks, agent memory, and the messy path to making agents actually useful.


// Bio

Sam Partee

Sam Partee is the CTO and Co-Founder of Arcade AI. Previously, a Principal Engineer leading the Applied AI team at Redis, Sam led the effort in creating the ecosystem around Redis as a vector database. He is a contributor to multiple OSS projects, including Langchain, DeterminedAI, LlamaInde,x, and Chapel, amongst others. While at Cray/HPE, he created the SmartSim AI framework, which is now used at national labs around the country to integrate HPC simulations like climate models with AI.


Rahul Parundekar

Rahul Parundekar is the founder of AI Hero. He graduated with a Master's in Computer Science from USC Los Angeles in 2010, and embarked on a career focused on Artificial Intelligence. From 2010-2017, he worked as a Senior Researcher at Toyota ITC, working on agent autonomy within vehicles. His journey continued as the Director of Data Science at FigureEight (later acquired by Appen), where he and his team developed an architecture supporting over 36 ML models and managing over a million predictions daily. Since 2021, he has been working on AI Hero, aiming to democratize AI access, while also consulting on LLMOps(Large Language Model Operations) and AI system scalability. Other than his full-time role as a founder, he is also passionate about community engagement, and actively organizes MLOps events in SF, and contributes educational content on RAG and LLMOps at learn.mlops.community.


// Related Links

Websites: arcade.dev // aihero.studio


~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

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Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Rahul on LinkedIn: /rparundekar

Connect with Sam on LinkedIn: /sampartee


Timestamps:

[00:00] Agents & Tools, Explained (Without Melting Your Brain)

[09:51] MVP Servers: Why Everything’s on Fire (and How to Fix It)

[13:18] Can We Actually Trust the Protocol?

[18:13] KYC, But Make It AI (and Less Painful)

[25:25] Web Automation Tests: The Bugs Strike Back

[28:18] MCP Dev: What Went Wrong (and What Saved Us)

[33:53] Social Login: One Button to Rule Them All

[39:33] What Even Is an AI-Native Developer?

[42:21] Betting Big on Smarter Models (High Risk, High Reward)

[51:40] Harrison’s Bold New Tactic (With Real-Life Magic Tricks)

[55:31] Async Task Handoffs: Herding Cats, But Digitally

[1:00:37] Getting AI to Actually Help Your Workflow

[1:03:53] The Infamous Varma System Error (And How We Dodge It)


Hosted by Demetrios, MLOps.community is a space for honest, meandering talks about the real work of making artificial intelligence systems actually work. This isn't about hype or theoretical papers; it's about the messy, practical, and often surprising journey of taking models from a notebook into a live environment. You'll hear from engineers and practitioners who are in the trenches, discussing the tools, the frustrations, and the occasional breakthroughs that define the day-to-day. The conversations are deliberately relaxed, covering everything from traditional machine learning pipelines to the new world of large language models and even the intangible "vibes" of team culture and process. Each episode peels back a layer on what "production" really means, whether that involves deploying a predictive service, managing an agentic system, or maintaining reliability as everything scales. Tuning into this podcast feels like grabbing a coffee with colleagues who aren't afraid to dig into the technical nitty-gritty while keeping the tone conversational and accessible. It's for anyone who builds, manages, or is just curious about the operational backbone that allows AI to deliver value, offering a grounded perspective often missing from the broader conversation.
Author: Language: en-us Episodes: 100

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