At SnowCamp 2026, with my Cast Codeurs buddy Emmanuel Bernard of Hexactgon, I had the chance to deliver a talk on AI standards and protocols, with a big focus on MCP (Model Context Protocol), and A2A (Agent 2 Agent Protocol).

Without further ado, here’s the slide deck we presented:

This talk is based on the Devoxx 2025 deep dive session that I delivered with Emmanuel and my colleague Mete Atamel. As the talk wasn’t recorded during SnowCamp, I’ll share with you the 3h-long video from Devoxx below:

First of all, dear readers, let me wish you a happy new year! This is my first post on this blog for 2026. I’m looking forward to continuing sharing interesting content with you.

During my holiday break, I wanted to put my recent Java implementation of the Gemini Interactions API to the test. I implemented and released it with the help of Antigravity. My colleague Shubham Saboo and Gargi Gupta wrote a tutorial on how to build an AI research agent with Google Interactions API & Gemini 3. I thought this was a great opportunity to replicate this example in Java using my Interactions API Java SDK.

Google and DeepMind have announced the Interactions API, a new way to interact with Gemini models and agents.

Here are some useful links to learn more about this new API:

• An announcement is available on Google’s Keywords blog:
  Interactions API: A unified foundation for models and agents
• A more detailed article is available on Google’s developers blog:
  Building agents with the ADK and the new Interactions API
• The newly released Gemini Deep Research agent is now available via the Interactions API as well:
  Build with Gemini Deep Research
• The official documentation of the Interactions API.

About the Interactions API

The Rationale and Motivation

The Interactions API was introduced to address a shift in AI development, moving from simple, stateless text generation to more complex, multi-turn agentic workflows. It serves as a dedicated interface for systems that require memory, reasoning, and tool use. It provides a unified interface for both simple LLM calls and more complex agent calls.

In this article, I’ll guide you through the process of programmatically interacting with a web browser using the new Computer Use model in Gemini 2.5 Pro. We’ll accomplish this in Java ☕ leveraging Microsoft’s powerful Playwright Java SDK to handle the browser automation.

The New Computer Use Model

Unveiled in this announcement article and made available in public preview last month, via the Gemini API on Google AI Studio and Vertex AI, Gemini 2.5 Pro introduces a pretty powerful “Computer Use” feature.

Continuing my journey with Javelit, after creating a frontend for “Nano Banana” to generate images and a chat interface for a LangChain4j-based Gemini chat model, I decided to see how I could integrate an ADK agent with a Javelit frontend.

The Javelit interface for an ADK search agent

A Javelit interface for an ADK search agent

Yesterday, I uncovered the Javelit project in this article where I built a small frontend to create and edit images with Google’s Nano Banana image model.

Have you ever heard of Javelit? It’s like Streamlit in the Python ecosystem, but for the Java developer! I was lucky that the project creator reached out and introduced me to this cool little tool!

Javelit is a tool to quickly build interactive app frontends in Java, particularly for data apps, but it’s not limited to them. It helps you quickly develop rapid prototypes, with a live-reload loop, so that you can quickly experiment and update the app instantly.

At Devoxx Belgium, I recently had the chance to present this new talk dedicated to ADK for Java, the open source Agent Development Kit framework developed by Google.

The presentation covered:

• an introduction to the notion of AI agents
• how to get started in a Java and Maven project
• how to create your first agent
• how to debug an agent via the Dev UI
• the coverage of the various tools (custom function tools, built-in tools like Google Search or code execution, an agent as tool, MCP tools)
• an overview of the different ways to combine agents into a multi-agent system: sub-agents, sequential agents, parallel agents, loop agents
• some details on the event loop and services (session and state management, artifacts, runner…)
• structured input / output schemas
• the various callbacks in the agent lifecycle
• the integration with LangChain4j (to give access to the plethora of LLMs supported by LangChain4j)
• the definition of agents via configuration in YAML
• the new long-term memory support
• the plugin system
• the new external code executors (via Docker containers or backed by Google Cloud Vertex AI)
• how to launch an agent with the Dev UI from JBang

Slides of the presentation

The slide deck of this session is embedded below:

Large Language Models (LLMs) are all becoming “multimodal”. They can process text, but also other “modalities” in input, like pictures, videos, or audio files. But models that output more than just text are less common…

Recently, I wrote about my experiments with Nano Banana 🍌 (in Java), a Gemini chat model flavor that can create and edit images. This is pretty handy in particular for interactive creative tasks, like for example a marketing assistant that would help you design a new product, by describing it, by futher tweaking its look, by exposing it in different settings for marketing ads, etc.

In previous articles, I explored how to create an MCP server with Micronaut by vibe-coding one, following the Model Context Protocol specification (which was a great way to better understand the underpinnings) and how to create an MCP server with Quarkus.

Micronaut lacked a dedicated module for creating MCP servers, but fortunately, recently Micronaut added official support for MCP, so I was eager to try it out!