Apr 20, 2026

Full Workshop: Build Your Own Deep Research Agents - Louis-François Bouchard, Paul Iusztin, Samridhi

This hands-on workshop details the construction of a sophisticated, dual-part AI system for producing high-quality technical content. It begins with an MCP-powered deep research agent that autonomously plans, searches the web, and analyzes sources like YouTube to synthesize a grounded research artifact. The second part is a constrained, deterministic writing workflow that transforms this research into polished, non-sloppy content using an innovative "Evaluator-Optimizer" pattern for iterative refinement. The session emphasizes crucial AI engineering principles, such as choosing between agentic and workflow-based architectures, and concludes with a deep dive into implementing practical observability and evaluation pipelines to ensure the system is both measurable and improvable.

Apr 20, 2026

What AI Agent Skills Are and How They Work

AI agents, powered by LLMs, excel at reasoning but lack the procedural knowledge required for real-world workflows. Martin Keen explains how the 'agent skills' open standard solves this by packaging step-by-step instructions, enabling agents to automate complex tasks efficiently and reliably.

Apr 09, 2026

Building Agentic Applications with Spring AI • Matthew Meckes • GOTO 2025

Matthew Meckes from AWS makes a compelling case for Java's central role in the future of enterprise AI. This talk explores how Spring AI empowers developers to build robust, production-ready agentic applications by integrating LLMs with existing Java services, moving beyond proofs-of-concept to solve real-world business problems.

Mar 24, 2026

AI Models as a Service: Powering Agentic AI, Privacy, & RAG

Cedric Clyburn explains the Models-as-a-Service (MaaS) pattern, detailing how organizations can build their own private AI infrastructure to deploy models like LLMs securely and at scale. He covers the benefits over public APIs, including cost control, data sovereignty, and lifecycle management, and outlines a technical architecture using Kubernetes, API gateways, and observability tools.

Mar 09, 2026

Is RAG Still Needed? Choosing the Best Approach for LLMs

Martin Keen compares Retrieval Augmented Generation (RAG) with the emerging long context window approach in LLMs. He analyzes the pros and cons of each, from infrastructure simplicity and retrieval accuracy to computational costs and the 'needle in the haystack' problem, providing guidance on when to use each solution.

Mar 09, 2026

OpenClaw's Memory Sucks and the fix is simple — Dhravya Shah, Supermemory

Dhravya Shah, founder of Super Memory, details the evolution of his company from a simple RAG-based consumer app to a sophisticated, open-source context infrastructure for AI, and introduces a novel hooks-based memory solution for OpenClaw.