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In this episode, Ayan Gupta is joined again by Julien Dubois, who takes AI development to the next level by showing you how to build multi-agent systems that work together. Imagine if your coffee cup could not only order a refill on its own but also coordinate with a barista to prepare it exactly how you like it, that’s the power of AI agents working in concert!
Building on the foundation from the last session, this session dives deep into creating three coordinated AI agents using LangChain4j. The first is an “Author” agent that writes poems using GPT-4o Mini. The second is an “Actor” agent that uses Mistral 3B and a text-to-speech tool called MaryTTS to transform poems into audio files. The third is a “Supervisor” agent that orchestrates the other two, ensuring they work together seamlessly.
Julien explains the two fundamental approaches to agent orchestration: Pure AI orchestration, where a supervisor LLM decides which agents to call and when, and Workflow-based orchestration using LangChain4j’s rich API to define sequences, loops, and parallel operations. For this demo, you’ll use the simpler workflow approach with a SequenceBuilder that chains the Author and Actor agents together.
The result? You ask for a poem about “Java Virtual Machine,” and the system automatically generates the poem with one agent, converts it to speech with another, and delivers an output.wav file you can play. This demonstrates the incredible potential of multi-agent systems for complex, real-world applications. This is advanced AI development made accessible!
Resources: aka.ms/JavaAndAIForBeginners
0:00 – Introduction: Agents That Act on Your Behalf
0:34 – What Is an AI Agent?
1:07 – Three-Agent System Overview
1:50 – Whiteboard Diagram: How Agents Work Together
2:30 – Two Approaches to Orchestration
3:30 – Pure AI vs Workflow-Based Orchestration
4:10 – Building the Author Agent
5:00 – Creating the Agent Interface
5:40 – Adding @UserMessage and @Agent Annotations
6:20 – Configuring the Author Service
7:10 – Testing the Author Agent Alone
8:00 – Building the Actor Agent
9:00 – Setting Up Mistral 3B Model
9:50 – Creating the Text-to-Speech Tool
10:30 – Running MaryTTS in Docker
11:20 – Implementing the @Tool Annotation
12:20 – Building the Supervisor Agent
13:00 – Creating a Shared Context Map
13:30 – Using SequenceBuilder for Orchestration
14:10 – Running the Complete Multi-Agent System
14:50 – Listening to the Generated Audio
15:36 – Session Recap and Summary
16:23 – Understanding Agent Specialization
16:52 – Wrap-Up and Resources
#AIAgents #LangChain4j #MultiAgent #JavaAI #AgentOrchestration #Mistral #TextToSpeech #IntelligentSystems #JavaDevelopment #OpenAI #DockerContainers #MaryTTS Read More Microsoft Developer
