Yesterday, we went bananas 🍌 creating and editing images with Nano Banana, in Java. Now, what about generating videos as well, still in Java, with Veo 3?

Especially since this week, Google announced that Veo 3 became generally available, with reduced pricing, a new 9:16 aspect ratio (nice for those vertical viral videos) and even with resolution up to 1080p!

In today’s article, we’ll see how to create videos, in Java, with the GenAI Java SDK. We’ll create videos either:

By now, you’ve all probably seen the incredible images generated by the Nano Banana model (also known as Gemini 2.5 Flash Image preview)? If you haven’t, I encourage you to play with it within Google AI Studio, and from the Gemini app. or have a look at the @NanoBanana X/Twitter account which shares some of its greatest creations.

As a Java developer, you may be wondering how you can integrate Nano Banana in your own LLM-powered apps. This is what this article is about! I’ll show you how you can use this model to:

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.

In the first article of this Advanced RAG series, I talked about an approach I called sentence window retrieval, where we calculate vector embeddings per sentence, but the chunk of text returned (and added in the context of the LLM) actually contains also surrounding sentences to add more context to that embedded sentence. This tends to give a better vector similarity than the whole surrounding context. It is one of the techniques I’m covering in my talk on advanced RAG techniques.

In a nutshell, the AI agent equation is the following:

AI Agent = LLM + Memory + Planning + Tool Use

AI agents are nothing without tools! And they are actually more than mere Large Language Model calls. They require some memory management to handle the context of the interactions (short term, long term, or contextual information like in the Retrieval Augmented Generation approach. Planning is important (with variations around the Chain-of-Thought prompting approach, and LLM with reasoning or thinking capabilities) for an agent to realize its tasks.

As I’m contributing to ADK (Agent Development Kit) for Java, and LangChain4j (the LLM orchestration framework) I interact with MCP (Model Context Protocol) servers and tools to further expand the capabilities of my LLMs.

Recently, I showed how to vibe-code an MCP server using Micronaut. You know I usually talk about Micronaut, but this time, I wanted to experiment with Quarkus, and in particular with its built-in support for implementing MCP servers.

Getting started with Quarkus’ MCP support

I created a brand new Quarkus project from IntelliJ IDEA, with its Quarkus template, and I added a couple key dependencies for JSON marshalling, but even more important, for the MCP support: