Daniel Oh
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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