Ben Morss is a Developer Advocate at DeepL — the German AI translation company — and over roughly ten days, he took Model Context Protocol on the road: 10 cities, 10 talks, 4 countries (US, Canada, France, Germany). Along the way, he went from "I didn't know what MCP was at all" to teaching packed rooms how to build a server from scratch. In this conversation with host Alex Saltkever at the MCP Dev Summit North America, he shares the one lesson that kept surprising everyone.

10 Cities. 4 Countries. One Unexpected MCP Lesson.

Cornelia Davis has spent three decades watching programming abstractions climb — from assembly to C to Java to the cloud. Now at Temporal (and author of the influential book Cloud Native Patterns), she argues that natural language is the most radical abstraction jump yet: the first one that isn't deterministic. Recorded at the MCP Dev Summit in New York, this conversation covers how durable execution becomes the safety net underneath probabilistic AI systems.

The Next Programming Language Is English

This is our final installment of a series that studies 20+ tools in the AI cognition landscape rated against organizational requirements. We present a tactical guide for choosing a memory foundation based on your specific needs for persistence, scale, knowledge base, and multimodal data.

AI Memory & Cognition Landscape: The Architect’s Playbook

Denny Lee is PM Director, Startups & Ecosystem at Databricks, a longtime Apache Spark, MLflow, and Delta Lake contributor — and one of the people behind Omnigent, the open-source meta-harness Databricks just released under Apache 2.0. He joins Demetrios to explain why the industry is moving from models to harnesses to meta-harnesses, why token spend is replaying the CapEx-to-OpEx shift all over again, and why he's using debating AI agents to plan a matcha farm in Taiwan.

Omnigent: Composition, Control, and Collaboration for AI Agents

<h1>Loop engineering is the shift from manually prompting AI agents turn-by-turn to designing autonomous systems that do the prompting for you. Instead of writing a prompt, reading output, then writing the next prompt, you build a small control system that discovers work, delegates tasks to agents (and sub-agents), verifies results, persists state, and decides what to do next on a schedule or until a goal is met. It sits one level above agent harness engineering: where a harness equips a single agent run, a loop keeps firing agents on a cadence, spawning helpers, and feeding itself. The framing is increasingly tool-agnostic, as both Claude Code and OpenAI Codex have converged on similar primitives. The people building at the frontier are already living it: Boris Cherny, head of Claude Code at Anthropic, has said his job is no longer writing prompts but writing the loops that do.</h1> <h1>This month, David DeStefano, Sam Christensen, and Valdimar Eggertsson will sit down for an hour and chat about some of their learnings and experiences exploring loop engineering. We'll also open it up to the audience, who may have their own experiences and insights we'd love to hear more from.</h1> <h2>📖 Reading Material</h2><h2>How to Create Loops with Claude</h2><h2>Please read before the discussion (recommended):</h2><h1>https://todatabeyond.substack.com/p/how-to-create-loops-with-claude-a?r=1sqbmi&amp;utm_campaign=post-expanded-share&amp;utm_medium=web&amp;triedRedirect=true</h1>

A Discussion on Loop Engineering

Session 18 is dedicated to one of the most valuable aspects of the Coding Agents Lunch &amp; Learn series: learning directly from the community. This special Community Show &amp; Tell session provides an opportunity for attendees to share the projects, workflows, tools, experiments, and discoveries they've been working on while exploring the world of coding agents and agentic engineering. The pace of innovation in this space continues to accelerate, and many of the most interesting breakthroughs come from practitioners experimenting with new ideas, combining tools in unexpected ways, and pushing existing workflows beyond their intended use cases. During this session, participants are encouraged to showcase what they've built, discuss challenges they've encountered, demonstrate useful techniques, and exchange lessons learned with fellow builders. Whether you're working on coding agents, multi-agent systems, evaluation frameworks, developer tooling, infrastructure, open-source projects, or entirely new approaches to software development, this is a chance to share your experiences and learn from others doing the same. No formal presentation is required—community discussion, live demonstrations, workflow walkthroughs, and informal knowledge sharing are all welcome. If you're looking for inspiration, feedback, or simply a chance to connect with other practitioners exploring the future of software development, this session is for you. Bring your projects, your questions, and your ideas as we close out another chapter of the Coding Agents Lunch &amp; Learn series together.

Coding Agents Lunch & Learn Session 18: Community Show & Tell

As coding agents become increasingly capable, the conversation is shifting from simply using agents to understanding how to build reliable, repeatable, and scalable agent-powered systems. Session 17 focuses on the engineering practices, evaluation techniques, and operational considerations that help transform promising agent workflows into dependable tools that teams can trust. Throughout this session, we'll discuss how practitioners are approaching agent development, including workflow design, prompt engineering, skill development, evaluation methodologies, observability, testing, and operational best practices. We'll explore common challenges teams encounter when working with agents and share lessons learned from both successful implementations and failures. Participants will gain insight into how experienced builders think about reliability, quality, and performance when deploying agent-assisted workflows at scale. In addition to practical discussions, we'll highlight emerging tools, frameworks, and techniques that are helping developers create more effective agent systems. Whether you're experimenting with coding agents for personal projects or deploying them across larger engineering organizations, this session aims to provide actionable ideas and valuable perspectives from people actively building in the space. Join us for an engaging conversation about what it takes to build, evaluate, and operate coding agents effectively in today's rapidly evolving ecosystem.

Coding Agents Lunch & Learn Session 17: Building, Evaluating & Operating Agents

Join us for Session 16 of the Coding Agents Lunch &amp; Learn series as we take a closer look at how developers, researchers, and engineering teams are integrating coding agents into their day-to-day workflows. While the capabilities of coding agents continue to evolve rapidly, understanding how to effectively incorporate them into real engineering processes remains one of the most important challenges facing teams today. In this session, we'll explore practical approaches to working with coding agents across the software development lifecycle. From planning and research to implementation, testing, debugging, and deployment, participants will have the opportunity to discuss the workflows that are delivering real value and the lessons learned from using these tools in production environments. We'll cover a range of experiences, from individual developers experimenting with new tools to teams building collaborative agent-driven workflows. As always, this Lunch &amp; Learn is designed to be highly interactive and community-driven. Attendees are encouraged to share their own experiences, challenges, successes, and discoveries. Whether you're just beginning your journey with coding agents or already operating sophisticated agent-assisted workflows, you'll leave with new ideas, practical insights, and a better understanding of how others are approaching this rapidly changing space. Bring your questions, your curiosity, and anything interesting you've been building.

Coding Agents Lunch & Learn Session 16: Real-World Coding Agent Workflows

AI agents are only as good as the information they can find, retrieve, and remember.Join us for a special community discussion with the Qdrant team as we explore the latest advances in agentic memory, vector search, retrieval systems, and production AI architectures. As agents evolve beyond simple chatbots into autonomous systems capable of reasoning across large amounts of information, retrieval has become one of the most critical layers in the AI stack.Together, we'll dive into the practical challenges of building systems that can remember what matters, forget what doesn't, and consistently retrieve the right context at the right moment. <h3>What we'll explore</h3> 🧠 Agentic Memory Architectures<ol><li data-list="bullet">Why memory is becoming a core component of production AI systems</li><li data-list="bullet">Managing long-term memory without bloating context windows</li><li data-list="bullet">Memory lifecycle management: what agents should remember, and what they should forget</li><li data-list="bullet">Practical patterns for persistent memory using vector databases</li></ol>🔍 Late Chunking &amp; Production RAG<ol><li data-list="bullet">Why traditional chunking strategies often fail</li><li data-list="bullet">Preserving document-level context and meaning</li><li data-list="bullet">Improving retrieval quality and relevance in real-world applications</li></ol>📊 Evaluating Search Quality<ol><li data-list="bullet">Measuring retrieval performance beyond simple accuracy metrics</li><li data-list="bullet">Semantic search vs exact-match retrieval</li><li data-list="bullet">Practical frameworks for evaluating search systems in production</li></ol>🌐 Embeddings Beyond Text<ol><li data-list="bullet">Retrieval for code, images, tables, time-series, and multimodal data</li><li data-list="bullet">Emerging patterns for next-generation AI applications</li><li data-list="bullet">Real-world examples from production deployments</li></ol>You’ll hear from an exceptional lineup of experts from Qdrant, including Neil Kanungo, Head of Developer Relations, alongside Ewa Szyszka and Dylan Couzon, both Developer Relations Engineers, and Evgeniya Sukhodolskaya, Senior Developer Advocate. Together, they bring deep experience in vector search, retrieval systems, RAG architectures, and production AI applications, offering practical insights from helping developers and organizations build scalable AI systems powered by semantic search and agentic workflows. Hosted by Demetrios Brinkmann, Chief Happiness Engineer at MLOps Community, this session promises an engaging mix of technical expertise, real-world lessons, and interactive discussion.Expect practical insights, real-world examples, audience Q&amp;A, and plenty of opportunities to engage directly with the engineers helping shape the future of vector search and AI retrieval systems.Whether you're building RAG applications, AI agents, search systems, or knowledge platforms, you'll leave with actionable ideas you can apply immediately.Because when AI systems fail, it's often not because they can't think.It's because they can't find what they need to know.

The Current State of Agentic Retrieval

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