My friend Leonard and I were collaborating over the weekend on some new updates for the Groovy Web Console. This console is an online playground where Apache Groovy users can run Groovy scripts online, with different versions of the language, from Groovy 3 up to the experimental Groovy 6. Additionally, there’s a specific integration with the Spock testing framework, which allows users to run tests written with the framework.

Here’s what the old console (well, the current one at the time of this writing) looks like:

Google recently announced Managed Agents in the Gemini API at Google I/O. This feature allows developers to run autonomous agents that “reason, plan, use tools, and execute code inside isolated cloud sandboxes”.

Rather than requiring developers to manually build, secure, and scale the execution environment (including sandbox containers, network routes, and runtime engines), the Gemini API handles this infrastructure. This is powered by the Antigravity agent running on Gemini 3.5 Flash.

This article shows how to implement these agentic capabilities in Java using the Gemini Interactions SDK.

With the launch of ADK for Kotlin, it means you can power up your Android applications with agents.

My colleague Jolanda Verhoef built an Android app that integrates an ADK agent written in Kotlin. It’s an app with a chat interface that lets you ask fun facts about anything you want.

At Devoxx France 2026 and JNation 2026, I had the pleasure of presenting a session on Agentic Design Patterns. In this talk, I explore how to move beyond basic LLM wrappers to build reliable, scalable, and sophisticated AI agent systems.

In the coming weeks, I’ll be blogging about some of these patterns, that I implemented using LangChain4j and ADK for Java.

Abstract

It’s time to dive into the deep end, far from “hello world” demos. To build your multi-agent systems, you often start by assembling classic bricks: sequential or parallel flows, or loops. The basics!

Read more...

A few days ago, Max Rydahl Andersen published a fascinating article about nanocode: a minimalist Claude Code alternative implemented in just 260 lines of Java (inspired from a 250-line Python equivalent). It was a masterclass in “leanness,” using raw HTTP calls and Jackson JSON parsing, an OpenRouter or Anthropic LLM endpoint, to create an autonomous coding loop.

I loved the concept, but I had a very practical motivation to take it in a different direction: I don’t have a Claude subscription. 😃

Today, DeepMind announced the release of Gemma 4, a very impressive and powerful new version of the Gemma family of models. As I’ve been contributing to ADK Java a fair bit recently, I was curious to see how I would configure ADK Java agents to work with Gemma 4.

In this article, we’ll explore 3 paths:

• Calling the AI Studio API surface directly,
• Calling Gemma 4 hosted via a vLLM instance thanks to the LangChain4j bridge.
• Calling Gemma 4 locally via Ollama

With the appropriate model weights format, we’ll also be able to run Gemma 4 locally via Ollama. But that’s for another day.

Since I started using Model Context Protocol (MCP) to equip my AI agents with useful tools, I’ve been looking for ways to quickly build and iterate on local servers. A few weeks ago, I shared how to easily build a local MCP server in Java with a custom skill in Gemini CLI. Today, I wanted to put that skill to the test by creating a Wikipedia MCP server.

What’s impressive is that I didn’t even have to leave my terminal or read documentation. The entire process was a conversation with Gemini CLI, leveraging its ability to search the web, find libraries, and even check migration guides!

I’m happy to echo here the release of ADK for Java v1.0, Google’s Agent Development Kit framework to build AI agents in Java. I spent a lot of time on this project. I also wrote the announcement blog post on the Google for Developers blog. And I’ve recorded this YouTube video highlighting some of the new features of the framework, in which I’m demonstrating some of them via an app I built: my Comic Trip agent (pun intended).

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:

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.