Daniel Oh

Supercharge Agentic AI Apps: A DevEx-Driven Approach to Cloud-Native Scaffolding

What if a single prompt could transform your generative AI service into a fully autonomous agent? See how it's done.

Supercharge Agentic AI Apps: A DevEx-Driven Approach to Cloud-Native Scaffolding
#1about 5 minutes

Understanding the evolution and autonomy of agentic AI

Agentic AI evolves from traditional AI by autonomously deciding which external tools and resources to use, unlike systems with predefined workflows.

#2about 3 minutes

Exploring frameworks for building agentic AI applications in Java

While Python has many agentic AI frameworks, Java developers can combine tools like Quarkus, Spring AI, and LangChain4j to build similar applications.

#3about 5 minutes

Scaffolding a new agentic AI project with Quarkus

Use the Quarkus application generator to quickly create a new project with dependencies for OpenAI and MCP, then run it in development mode for live coding.

#4about 3 minutes

Defining the agent's behavior with a Java interface

A simple Java interface with LangChain4j annotations and a carefully crafted system prompt can instruct the AI model to autonomously utilize any available tools.

#5about 5 minutes

Configuring and auto-starting MCP servers in Quarkus

Configure external tools like search, maps, and messaging by defining MCP servers in the application properties file, which Quarkus automatically downloads and runs.

#6about 4 minutes

Demonstrating a multi-tool agent finding and sharing information

A live demonstration shows the agent autonomously using search, maps, and Slack tools to fulfill a complex user request for finding and sharing restaurant recommendations.

#7about 3 minutes

Using software templates to share agentic AI applications

Simplify multi-agent development and collaboration by using an Internal Developer Platform (IDP) like Backstage, with Quarkus automatically generating software templates from your project.

#8about 2 minutes

The future roadmap for MCP and key takeaways

The MCP roadmap includes a server registry and multi-modal support, and the key takeaway is that Quarkus simplifies complex agentic AI development for Java developers.

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Matching moments

Demo of an AI assistant using LangChain4j and Quarkus
04:44 MIN

Demo of an AI assistant using LangChain4j and Quarkus

Create AI-Infused Java Apps with LangChain4j

Demoing an AI assistant for infrastructure as code
05:25 MIN

Demoing an AI assistant for infrastructure as code

Forget Developer Platforms, Think Developer Productivity!

Adopting a modern tech stack for faster development
03:23 MIN

Adopting a modern tech stack for faster development

How to Destroy a Monolith?

Integrating generative AI into Java applications with LangChain4j
03:10 MIN

Integrating generative AI into Java applications with LangChain4j

Infusing Generative AI in your Java Apps with LangChain4j

Demo of scaffolding an AI app with Developer Hub
04:01 MIN

Demo of scaffolding an AI app with Developer Hub

Developer Experience, Platform Engineering and AI powered Apps

Demo of automated code transformation with Kai
10:56 MIN

Demo of automated code transformation with Kai

Application Modernization Leveraging Gen-AI for Automated Code Transformation

Demo: Building an agentic app with external tools
04:13 MIN

Demo: Building an agentic app with external tools

Self-Hosted LLMs: From Zero to Inference

Scaffolding a new application with live coding
04:06 MIN

Scaffolding a new application with live coding

Developer Joy with Quarkus

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