An exploration of three distinct architectural approaches to building AI agents in Go: low-level integration with the GenAI SDK, middleware-driven flows using Genkit, and autonomous agent orchestration with the Agent Development Kit (ADK).
A practical deep dive into rendering interactive agent interfaces using A2UI and Web Components with ADK, covering architecture patterns, real-world lessons, and common pitfalls.
Presentation on developing autonomous AI agents in Go, walking through design patterns from low-level API calls with the GenAI SDK to structured workflows with Genkit and full agent systems with ADK.
Hands-on workshop demonstrating how to transform standard text-based LLM chatbots into interactive, rich web applications using the Agent Development Kit (ADK), AG-UI protocol, and A2UI to map agent outputs directly to dynamic web components.
This talk explores how to leverage ADK (Assistant Development Kit) and Gemini to create a personalized executive assistant. Attendees will learn the practical steps and tools involved in developing an AI-powered assistant to streamline their workflow.
Agent Development Kit (ADK) is an open source agent development framework built by Google. In this talk we are going to see how to leverage Vertex AI to make production-ready agents that include features like memory, session management, artifact management, RAG and others. The session will walk you through the development of an “Executive Assistant” agent that will learn not only about your tasks, but your preferences. Even if you have never built an agent before, in this talk you will learn how to quickly ramp up using Gemini CLI, the developer knowledge MCP and codewiki, for a truly modern agent development experience.
A deep dive into the design and implementation of GoDoctor, a specialized Model Context Protocol (MCP) server for Go. This talk explores how giving agents specialized tools can significantly improve their effectiveness and reliability.
Inspired by the "computer" in Star Trek, in this talk we are going to create a natural language diagnostic agent that can answer questions about your computer, from asking the number of cores and amount of memory installed to running a "Level 1 Diagnostic Procedure". This AI agent will be built step by step using Python, Vertex AI, Gemini and OSQuery.
Inspired by the "computer" in Star Trek, in this talk we are going to create a natural language diagnostic agent that can answer questions about your computer, from asking the number of cores and amount of memory installed to running a "Level 1 Diagnostic Procedure".
Hands-on workshop on building and deploying a Model Context Protocol (MCP) server to extend the capabilities of the Gemini CLI, transforming it into a specialized development expert.
Keynote presentation introducing the Model Context Protocol (MCP), exploring its core concepts, architecture, and the building blocks used to create AI-enabled applications with Go.
Hands-on workshop on building and deploying a Model Context Protocol (MCP) server to extend the capabilities of the Gemini CLI, transforming it into a specialized development expert.
Inspired by the "computer" in Star Trek, in this talk we are going to create a natural language diagnostic agent that can answer questions about your computer, from asking the number of cores and amount of memory installed to running a "Level 1 Diagnostic Procedure".
The talk explores the evolving role of Gophers in the age of AI, focusing on extending language models with custom tools and integrating AI as a powerful extension of skills, using the Model Context Protocol (MCP) and live Go demonstrations.
Keynote presentation at The National Museum of Computing exploring the Model Context Protocol (MCP), its core architecture, and how AI agents and developer tooling are shaping the technical job roles of 2030.
This talk demonstrates how to build an AI agent step-by-step using Python, Vertex AI, Gemini, and Osquery. It explores how Osquery, a tool for exposing OS information via SQL, can be leveraged by large language models to enable advanced diagnostics through natural language queries, eliminating the need to memorize complex command lines.
Keynote presentation at GopherCon UK 2025. An introduction to the Model Context Protocol (MCP), exploring its core concepts, architecture, and the building blocks used to create AI-enabled applications with Go.
When your legacy codebase holds you back, nuke it and start over. Here is how I replaced a clunky Python RAG pipeline with a compiled Genkit Go agent and Agent Skills.
What does it actually take to build an autonomous agent in Go? We implement the same Retro Game Appraiser three ways to compare the Go GenAI SDK, Genkit, and ADK.
Learn how to make your AI agent completely offline. We walk through swapping the cloud model for a local Qwen 2.5 via Ollama, and building a local RAG knowledge base using SQLite and sqlite-rag to query Osquery schemas and packs.
Elevate your Google ADK agent with a custom, retro-style interface. This step-by-step guide shows you how to replace the standard Dev-UI using FastAPI and vanilla JavaScript, adding personality with a real-time streaming, AI-generated avatar.
This article is a guide to creating a diagnostic agent with the Agent Development Kit (ADK). It covers the development process and explains how to use Vertex AI RAG to improve the agent’s response quality.
Based on my keynote at Gophercon UK 2025, this article is an introduction to the Model Context Protocol (MCP), exploring its core concepts, architecture, and the building blocks — Tools, Prompts, and Resources — used to create AI-enabled applications with Go.
