
Learn basic input validation and error handling in a Spring Boot Elasticsearch app, including bad request exceptions, problem detail responses (RFC 9457), and simple query building for a suggestions API.
Explore building a reactive order service in Java, implementing an orchestrator and steps for payment, inventory, and shipping, with DTO mapping and workflow actions.
Explore three main components in Java for agentic AI by mastering Elasticsearch queries, fuzziness, match phrase, and multi-field mapping with bool queries and result highlighting.
Demonstrates how Elasticsearch matching works through single and multi-match queries, fuzziness, and phrase queries, including highlighting, multi-field mapping, and a bool query to refine results.
Explore configuring kafka binder in spring cloud stream with receiver options customizer and binding properties, including group instance id, to configure consumer properties; test with embedded kafka.
Learn to build a Java Spring microservice: a customer service with DTOs and REST calls for movie recommendations, patch endpoints, exception handling, Docker Compose, and Postgres.
Design CIDR-based VPC with public and private subnets, routes, and NAT gateways. Build a Spring Boot movie service with Postgres and test containers, including health checks.
Learn how to test and implement saga-based workflows in a Java microservices environment, validating compensating transactions with inventory and payment events, and contrasting choreography with the orchestrator pattern.
Configure a global content delivery distribution with CloudFront, using S3 or load balancer origins, and secure access via origin access control, Route 53 DNS, and SSL certificates.
Master the next CATCertified Foundation by learning how to build intelligent, autonomous AI agents using Java. This comprehensive course takes you from the fundamentals of Agentic AI to developing advanced AI-powered applications capable of reasoning, planning, decision-making, tool usage, and multi-agent collaboration. You will learn many concepts from Agentic AI with Java Programming in this course from basics to more advanced concepts.
You'll begin by understanding the core concepts behind Large Language Models (LLMs), prompt engineering, AI agents, and autonomous workflows. Then, you'll use modern Java frameworks and libraries to integrate LLMs, connect external tools, build Retrieval-Augmented Generation (RAG) applications, implement memory, orchestrate agent workflows, and develop real-world AI solutions.
you'll build real-world projects that demonstrate how Agentic AI can automate workflows, develop intelligent assistants, process documents, generate code, analyze data, and power enterprise applications. Each module focuses on practical implementation, enabling you to gain the skills needed to design, build, and deploy production-ready AI solutions.
Through hands-on projects and practical examples, you'll learn how to create production-ready Agentic AI applications that automate business processes, analyze documents, interact with APIs, and solve complex tasks. Whether you're a Java developer, software engineer, or AI enthusiast, this course provides the skills needed to build enterprise-grade AI applications using the Java ecosystem. By the end of this course, you'll have the confidence to design, develop, deploy, and scale intelligent AI agents with Java for modern software applications.