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, LlamaIndex 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 ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

MLOps Swag/Merch: [https://shop.mlops.community/]

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)

AI in M&A: Building, Buying, and the Future of Dealmaking // MLOps Podcast #315 with Kison Patel, CEO and M&A Science at DealRoom .

Join the Community: https://go.mlops.community/YTJoinIn

Get the newsletter: https://go.mlops.community/YTNewsletter

// Abstract

The intersection of M&A and AI, exploring how the DealRoom team developed AI capabilities and the practical use cases of AI in dealmaking. Discuss the evolving landscape of AI-driven M&A, the factors that make AI companies attractive acquisition targets, and the key indicators of success in this space.

// Bio

Kison Patel is the Founder and CEO of DealRoom, an M&A lifecycle management platform designed for buyer-led M&A and recognized twice on the Inc. 5000 Fastest Growing Companies list. He also founded M&A Science, a global community offering courses, events, and the top-rated M&A Science podcast with over 2.25 million downloads.

Through the podcast, Kison shares actionable insights from top M&A experts, helping professionals modernize their approach to deal-making. He is also the author of *Agile M&A: Proven Techniques to Close Deals Faster and Maximize Value*, a guide to tech-enabled, adaptive M&A practices.

Kison is dedicated to disrupting traditional M&A with innovative tools and education, empowering teams to drive greater efficiency and value.

// Related Links

Website: https://dealroom.net

https://www.mascience.com

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join our Slack community [https://go.mlops.community/slack]

Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)]

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MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Kison on LinkedIn: /kisonpatel

Timestamps:

[00:00] Kison's preferred coffee

[00:11] Takeaways

[00:40] Founders Journey Slumps

[05:07] Jira for M&A

[10:57] Overcoming Idea Paralysis

[14:32] Customer-led Discovery Success

[22:20] Legal Fees in Deals

[26:24] Data Room Differentiators

[29:26] PLG vs Sales Teams

[31:43] AI Pricing Strategies

[35:15] PLG AI Cost Optimization

[40:53] Building AI Teams

[47:40] Great Companies Are Bought

[51:10] M&A Failures and Fever

[54:23] Wrap up

AI, Marketing, and Human Decision Making // MLOps Podcast #313 with Fausto Albers, AI Engineer & Community Lead at AI Builders Club.

Join the Community: https://go.mlops.community/YTJoinIn

Get the newsletter: https://go.mlops.community/YTNewsletter

// Abstract

Demetrios and Fausto Albers explore how generative AI transforms creative work, decision-making, and human connection, highlighting both the promise of automation and the risks of losing critical thinking and social nuance.

// Bio

Fausto Albers is a relentless explorer of the unconventional—a techno-optimist with a foundation in sociology and behavioral economics, always connecting seemingly absurd ideas that, upon closer inspection, turn out to be the missing pieces of a bigger puzzle. He thrives in paradox: he overcomplicates the simple, oversimplifies the complex, and yet somehow lands on solutions that feel inevitable in hindsight. He believes that true innovation exists in the tension between chaos and structure—too much of either, and you’re stuck.

His career has been anything but linear. He’s owned and operated successful restaurants, served high-stakes cocktails while juggling bottles on London’s bar tops, and later traded spirits for code—designing digital waiters, recommender systems, and AI-driven accounting tools. Now, he leads the AI Builders Club Amsterdam, a fast-growing community where AI engineers, researchers, and founders push the boundaries of intelligent systems.

Ask him about RAG, and he’ll insist on specificity—because, as he puts it, discussing retrieval-augmented generation without clear definitions is as useful as declaring that “AI will have an impact on the world.” An engaging communicator, a sharp systems thinker, and a builder of both technology and communities, Fausto is here to challenge perspectives, deconstruct assumptions, and remix the future of AI.

// Related Links

Website: aibuilders.club

Moravec's paradox: https://en.wikipedia.org/wiki/Moravec%27s_paradox?utm_source=chatgpt.com

Behavior Modeling, Secondary AI Effects, Bias Reduction & Synthetic Data // Devansh Devansh // #311: https://youtu.be/jJXee5rMtHI

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

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Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Fausto on LinkedIn: /stepintoliquid

Timestamps:

[00:00] Fausto's preferred coffee

[00:26] Takeaways

[01:18] Automated Ad Creative Generation

[07:14] AI in Marketing Workflows

[13:23] MCP and System Bottlenecks

[21:45] Forward Compatibility vs Optimization

[29:57] Unlocking Workflow Speed

[33:48] AI Dependency vs Critical Thinking

[37:44] AI Realism and Paradoxes

[42:30] Outsourcing Decision-Making Risks

[46:22] Human Value in Automation

[49:02] Wrap up

MLOps with Databricks // MLOps Podcast #314 with Maria Vechtomova, MLOps Tech Lead | Founder at Ahold Delhaize | Marvelous MLOps.

Join the Community: https://go.mlops.community/YTJoinIn

Get the newsletter: https://go.mlops.community/YTNewsletter

// Abstract

The world of MLOps is very complex as there is an endless amount of tools serving its purpose, and it is very hard to get your head around it. Instead of combining various tools and managing them, it may make sense to opt for a platform instead. Databricks is a leading platform for MLOps. In this discussion, I will explain why it is the case, and walk you through Databricks MLOps features.

// Bio

Maria is an MLOps Tech lead with over 10 years of experience in Data and AI.

For the last 8 years, Maria has focused on MLOps and helped to establish MLOps best practices at large corporations.

Together with her colleague, she co-founded Marvelous MLOps to share knowledge on MLOps via training, social media posts, and blogs.

// Related Links

Website: marvelousmlops.io

MLOps Course discount code: MLOPS100 for the podcast listeners - https://maven.com/marvelousmlops/mlops-with-databricks?promoCode=MLOPS100

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join our slack community [https://go.mlops.community/slack]

Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)]

Sign up for the next meetup: [https://go.mlops.community/register]

MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Maria on LinkedIn: /maria-vechtomova

Timestamps:

[00:00] Maria's preferred coffee

[00:42] Takeaways

[02:48] Why Databricks for MLOps

[09:56] Platform Adoption vs Procurement Pain

[12:56] Databricks Best Practices

[16:57] Feature Store Overview

[22:00] Managed system trade-offs

[29:15] Databricks Developments and Trends

[44:31] Insider Info and Summit

[45:47] Data Ownership Pros and Cons

[48:08] Data Contracts and Challenges

[51:25] MLOps Databricks Book Guide

[52:19] Wrap up

AI, Marketing, and Human Decision Making // MLOps Podcast #313 with Fausto Albers, AI Engineer & Community Lead at AI Builders Club.

Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract

Demetrios and Fausto Albers explore how generative AI transforms creative work, decision-making, and human connection, highlighting both the promise of automation and the risks of losing critical thinking and social nuance.

// Bio

Fausto Albers is a relentless explorer of the unconventional—a techno-optimist with a foundation in sociology and behavioral economics, always connecting seemingly absurd ideas that, upon closer inspection, turn out to be the missing pieces of a bigger puzzle. He thrives in paradox: he overcomplicates the simple, oversimplifies the complex, and yet somehow lands on solutions that feel inevitable in hindsight. He believes that true innovation exists in the tension between chaos and structure—too much of either, and you’re stuck.

His career has been anything but linear. He’s owned and operated successful restaurants, served high-stakes cocktails while juggling bottles on London’s bar tops, and later traded spirits for code—designing digital waiters, recommender systems, and AI-driven accounting tools. Now, he leads the AI Builders Club Amsterdam, a fast-growing community where AI engineers, researchers, and founders push the boundaries of intelligent systems.

Ask him about RAG, and he’ll insist on specificity—because, as he puts it, discussing retrieval-augmented generation without clear definitions is as useful as declaring that “AI will have an impact on the world.” An engaging communicator, a sharp systems thinker, and a builder of both technology and communities, Fausto is here to challenge perspectives, deconstruct assumptions, and remix the future of AI.

// Related Links

Website: aibuilders.club

Moravec's paradox: https://en.wikipedia.org/wiki/Moravec%27s_paradox?utm_source=chatgpt.com

Behavior Modeling, Secondary AI Effects, Bias Reduction & Synthetic Data // Devansh Devansh // #311: https://youtu.be/jJXee5rMtHI~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join our slack community [https://go.mlops.community/slack]

Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)]

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MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Fausto on LinkedIn: /stepintoliquid

Making AI Reliable is the Greatest Challenge of the 2020s // MLOps Podcast #312 with Alon Bochman, CEO of RagMetrics.

Join the Community: https://go.mlops.community/YTJoinIn

Get the newsletter: https://go.mlops.community/YTNewsletter

Huge shout-out to RagMetrics for sponsoring this episode!

// Abstract

Demetrios talks with Alon Bochman, CEO of RagMetrics, about testing in machine learning systems. Alon stresses the value of empirical evaluation over influencer advice, highlights the need for evolving benchmarks, and shares how to effectively involve subject matter experts without technical barriers. They also discuss using LLMs as judges and measuring their alignment with human evaluators.

// Bio

Alon is a product leader with a fintech and adtech background, ex-Google, ex-Microsoft. Co-founded and sold a software company to Thomson Reuters for $30M, grew an AI consulting practice from 0 to over $ 1 Bn in 4 years. 20-year AI veteran, winner of three medals in model-building competitions. In a prior life, he was a top-performing hedge fund portfolio manager.Alon lives near NYC with his wife and two daughters. He is an avid reader, runner, and tennis player, an amateur piano player, and a retired chess player.

// Related Links

Website: ragmetrics.ai

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join our Slack community [https://go.mlops.community/slack]

Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)]

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MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Alon on LinkedIn: /alonbochman

Timestamps:

[00:00] Alon's preferred coffee

[00:15] Takeaways

[00:47] Testing Multi-Agent Systems

[05:55] Tracking ML Experiments

[12:28] AI Eval Redundancy Balance

[17:07] Handcrafted vs LLM Eval Tradeoffs

[28:15] LLM Judging Mechanisms

[36:03] AI and Human Judgment

[38:55] Document Evaluation with LLM

[42:08] Subject Matter Expertise in Co-Pilots

[46:33] LLMs as Judges

[51:40] LLM Evaluation Best Practices

[55:26] LM Judge Evaluation Criteria

[58:15] Visualizing AI Outputs

[1:01:16] Wrap up

Behavior Modeling, Secondary AI Effects, Bias Reduction & Synthetic Data // MLOps Podcast #311 with Devansh Devansh, Head of AI at Stealth AI Startup.

Join the Community: https://go.mlops.community/YTJoinIn

Get the newsletter: https://go.mlops.community/YTNewsletter

// Abstract

Open-source AI researcher Devansh Devansh joins Demetrios to discuss grounded AI research, jailbreaking risks, Nvidia’s Gretel AI acquisition, and the role of synthetic data in reducing bias. They explore why deterministic systems may outperform autonomous agents and urge listeners to challenge power structures and rethink how intelligence is built into data infrastructure.

// Bio

The best meme-maker in Tech. Writer on AI, Software, and the Tech Industry.

// Related Links

Subscribe to Artificial Intelligence Made Simple: https://artificialintelligencemadesimple.substack.com/

https://www.linkedin.com/pulse/alternative-ways-build-ai-models-taoist-devansh-devansh-z9iff/?trackingId=TKvUBldml6rOQUjqam%2B7lA%3D%3D

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

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MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Devansh on LinkedIn: /devansh-devansh-516004168

Timestamps:

[00:00] Devansh's preferred coffee

[01:23] Jailbreaking DeepSeek

[02:24] AI Made Simple

[07:16] Leveraging AI for Data Insights

[10:42] Synthetic Data and LLMs

[19:29] AI Experience Design

[22:20] Synthetic Data Bias Reduction

[26:33] Data Ecosystem Insights

[29:50] Moving Intelligence to Data Layer

[36:37] Minimizing Model Responsibility

[40:04] Workflow vs Generalized Agents

[49:24] AI Second-Order Effects

[55:26] AI Experience vs Efficiency

[1:01:10] Wrap up

GraphBI: Expanding Analytics to All Data Through the Combination of GenAI, Graph, & Visual Analytics // MLOps Podcast #310 with Paco Nathan, Principal DevRel Engineer at Senzing & Weidong Yang, CEO of Kineviz.

Join the Community: https://go.mlops.community/YTJoinIn

Get the newsletter: https://go.mlops.community/YTNewsletter

// Abstract

Existing BI and big data solutions depend largely on structured data, which makes up only about 20% of all available information, leaving the vast majority untapped. In this talk, we introduce GraphBI, which aims to address this challenge by combining GenAI, graph technology, and visual analytics to unlock the full potential of enterprise data.

Recent technologies like RAG (Retrieval-Augmented Generation) and GraphRAG leverage GenAI for tasks such as summarization and Q&A, but they often function as black boxes, making verification challenging. In contrast, GraphBI uses GenAI for data pre-processing—converting unstructured data into a graph-based format—enabling a transparent, step-by-step analytics process that ensures reliability.

We will walk through the GraphBI workflow, exploring best practices and challenges in each step of the process: managing both structured and unstructured data, data pre-processing with GenAI, iterative analytics using a BI-focused graph grammar, and final insight presentation. This approach uniquely surfaces business insights by effectively incorporating all types of data.

// Bio

Paco Nathan

Paco is a "player/coach" who excels in data science, machine learning, and natural language, with 40 years of industry experience. He leads DevRel for the Entity Resolved Knowledge Graph practice area at Senzing.com and advises Argilla.io, Kurve.ai, KungFu.ai, and DataSpartan.co.uk, and is lead committer for the pytextrank​ and kglab​ open source projects. Formerly: Director of Learning Group at O'Reilly Media; and Director of Community Evangelism at Databricks.

Weidong Yang

Weidong Yang, Ph.D., is the founder and CEO of Kineviz, a San Francisco-based company that develops interactive visual analytics based solutions to address complex big data problems. His expertise spans Physics, Computer Science and Performing Art, with significant contributions to the semiconductor industry and quantum dot research at UC, Berkeley and Silicon Valley. Yang also leads Kinetech Arts, a 501(c) non-profit blending dance, science, and technology. An eloquent public speaker and performer, he holds 11 US patents, including the groundbreaking Diffraction-based Overlay technology, vital for sub-10-nm semiconductor production.

// Related Links

Website: https://www.kineviz.com/

Blog: https://medium.com/kineviz

Website: https://derwen.ai/pacohttps://huggingface.co/pacoid

https://github.com/ceterihttps://neo4j.com/developer-blog/entity-resolved-knowledge-graphs/

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join our Slack community [https://go.mlops.community/slack]

Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)]

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Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Weidong on LinkedIn: /yangweidong/

Connect with Paco on LinkedIn: /ceteri/

AI Data Engineers - Data Engineering after AI // MLOps Podcast #309 with Vikram Chennai, Founder/CEO of Ardent AI.

Join the Community: https://go.mlops.community/YTJoinIn

Get the newsletter: https://go.mlops.community/YTNewsletter

// Abstract

A discussion of Agentic approaches to Data Engineering. Exploring the benefits and pitfalls of AI solutions and how to design product-grade AI agents, especially in data.

// Bio

Second Time Founder. 5 years building Deep learning models. Currently, AI Data Engineers

// Related Links

Website: tryardent.com

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join our Slack community [https://go.mlops.community/slack]

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MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Vikram on LinkedIn: /vikram-chennai/

Timestamps:

[00:00] Vikram's preferred coffee

[00:09] Takeaways

[00:42] Please like, share, leave a review, and subscribe to our MLOps channels! You can give us up to 5 stars on Spotify and leave your reviews!

[01:53] Product User Categories

[02:47] AI Data Engineer Role

[05:40] AI Coding Limits Enterprise

[09:22] Creating Feedback Loops

[14:23] Breaking Down Big Tasks

[19:39] Marketing Data Agent Scope

[28:03] Clear Success Metrics

[32:20] Creating Agent Glossary

[36:43] AI Prompt Toolkits

[38:54] Pricing Strategy Discussion

[43:20] Compute Abstraction and Pipelines

[45:23] Agent Surprises and Logs

[47:12] Wrap up

I am once again asking "What is MLOps?" // MLOps Podcast #308 with Oleksandr Stasyk, Engineering Manager, ML Platform of Synthesia.

Join the Community: https://go.mlops.community/YTJoinIn

Get the newsletter: https://go.mlops.community/YTNewsletter

// Abstract

What does it mean to MLOps now? Everyone is trying to make a killing from AI, everyone wants the freshest technology to show off as part of their product. But what impact does that have on the "journey of the model". Do we still think about how an idea makes it's way to production to make money? How can we get better at it, maybe the answer lies in the ancient "non-AI" past...

// Bio

For the majority of my career I have been a "full stack" developer with a leaning towards devops and platforms. In the last four years or so, I have worked on ML Platforms. I find that applying good software engineering practises is more important than ever in this AI fueled world.

// Related Links

Blogs: https://medium.com/@sashman90/mlops-the-evolution-of-the-t-shaped-engineer-a4d8a24a4042

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join our slack community [https://go.mlops.community/slack]

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MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Sash on LinkedIn: /oleksandr-stasyk-5751946b

Timestamps:

[00:00] Sash's preferred coffee

[00:09] Takeaways

[01:21] Vibe Coding Reality Check

[06:27] MLOps and Vibe Coding

[12:53] Data Engineering in GenAI

[14:53] MLOps in MVP Development

[21:13] Platform Engineering Org Models

[27:30] Empathy in Data Engineering

[31:11] Post-DevOps MLOps Evolution

[39:32] AI for Fast Feedback

[46:53] AI Workflow vs Real Work

[50:13] ML Confession Stories

[59:06] Shift Left in Testing

[1:05:49] Wrap up