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!

Note: For the impatient, you can checkout the code we’ll be covering in this article on GitHub.

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I often find myself staring at a wall of text online. It could be a lengthy technical article, a detailed news report, or a deep-dive blog post. My first thought is often: “Is this worth the time to read in full?” On top of that, for my podcast, Les Cast Codeurs, I’m constantly gathering links and need to create quick shownotes, which is essentially… a summary.

My first attempt to solve this was a custom Gemini Gems I created: a personalized chatbot that could summarize links. It worked, but I often ran into a wall: it couldn’t access paywalled content, pages that required a login, or dynamically generated sites that I was already viewing in my browser. The solution was clear: I needed to bring the summarization to the content, not the other way around. The idea for a Chrome extension was born.

Let me share an interesting experiment I worked on to visualize your AI agent structure, more specifically, Agent Development Kit (ADK) multiagents.

The more complex your agents become, as you split tasks and spin off more specialized and focused sub-agents, the harder it is to see what your system is really made of, and how the interactions happen between the various components.

This is also something I experienced when I was covering Google Cloud Workflows: the more steps in the workflow, the more loops I had, indirections, conditions, etc, the trickier it was to understand and debug. And sometimes, as the saying goes, a picture is worth a thousand words. So when I was working on my recent series of articles on ADK agentic workflows (drawing diagrams by hand) this idea of experimenting with an ADK agent visualizer came up immediately.

Over the past few articles, we’ve taken a deep dive into the powerful agentic workflow orchestration capabilities of the Agent Development Kit (ADK) for Java. We’ve seen how to build robust, specialized AI agents by moving beyond single, monolithic agents. We’ve explored how to structure our agents for:

• Part 1: Flexibility with sub-agents — Letting an orchestrator LLM decide the best course of action.
• Part 2: Order with sequential agents — Enforcing a strict, predictable execution path.
• Part 3: Efficiency with parallel agents — Running independent tasks concurrently to save time.
• Part 4: Refinement with loop agents — Creating iterative processes for self-correction and complex problem-solving.

In this final post, let’s bring it all together. We’ll summarize each pattern, clarify when to use one over the other, and show how their true power is unlocked when you start combining them.

Welcome to the final installment of our series on mastering agentic workflows with the ADK for Java. We’ve covered a lot of ground:

• Part 1: Sub-agents for flexible, user-driven delegation.
• Part 2: Sequential agents for predictable, ordered processes.
• Part 3: Parallel agents for efficient, concurrent execution.

Now, we’ll explore a pattern that enables agents to mimic a fundamental human problem-solving technique: iteration. For tasks that require refinement, trial-and-error, and self-correction, the ADK provides a LoopAgent.

Let’s continue our exploration of ADK for Java (Agent Development Kit for building AI agents). In this series, we’ve explored two fundamental agentic workflows:

• First, we used LlmAgent with sub-agents to create a flexible, manager-and-specialists model.
• Then, we used the SequentialAgent to enforce a strict, linear order of operations.

But what if your problem isn’t about flexibility or a fixed sequence? What if it’s about efficiency? Some tasks don’t depend on each other and can be done at the same time. Why wait for one to finish before starting the next?

In the first part of this series, we explored the “divide and conquer” strategy using sub-agents to create a flexible, modular team of AI specialists. This is perfect for situations where the user is in the driver’s seat, directing the flow of conversation. But what about when the process itself needs to be in charge?

Some tasks are inherently linear. You have to do Step A before Step B, and Step B before Step C. Think about a CI/CD pipeline: you build, then you test, then you deploy. You can’t do it out of order… or if you do, be prepared for havoc!

Let me come back to the Agent Development Kit (ADK) for Java! We recently discussed the many ways to expand ADK agents with tools. But today, I want to explore the multi-agentic capabilities of ADK, by talking about sub-agent workflows.

In upcoming articles in this series, we’ll also talk about sequential, parallel, and loop flows.

The “divide and conquer” strategy

Think of building a complex application. You wouldn’t put all your logic in a single, monolithic class, would you? You’d break it down into smaller, specialized components. The sub-agent workflow applies this same “divide and conquer” principle to AI agents.

I recently gave a talk titled “AI Agents, the New Frontier for LLMs”. The session explored how we can move beyond simple request-response interactions with Large Language Models to build more sophisticated and autonomous systems.

If you’re already familiar with LLMs and Retrieval Augmented Generation (RAG), the next logical step is to understand and build AI agents.

What makes a system “agentic”?

An agent is more than just a clever prompt. It’s a system that uses an LLM as its core reasoning engine to operate autonomously. The key characteristics that make a system “agentic” include: