This talk outlines the current status and roadmap of the Jakarta Agentic AI specification.

AI agents are one of the most prominent developments in enterprise and cloud native computing in decades. They promise to fundamentally accelerate innovation, automation, and productivity by leveraging AI in virtually every industry. Agents operate by leveraging Neural Networks, Machine Learning (ML), Natural Language Processing (NLP), Large Language Models (LLMs), and many other AI technologies to aim to perform specific tasks autonomously with little or no human intervention. They detect events, gather data, generate self-correcting plans, execute actions, process results, and evolve subsequent decisions. Examples include self-driving cars, security monitors, Site Reliability Engineering (SRE) agents, stock monitors, code/application generators, health monitors, customer service agents, manufacturing robots, and many others.

The Agentic AI specification aims to provide a set of APIs that make it easy, consistent, and reliable to build, deploy, and run AI agents on Jakarta EE runtimes. The technology aims to do for developing AI agents what Servlet did for HTTP processing, Jakarta REST did for RESTful web services, or perhaps most appropriately, Jakarta Batch did for batch processing. It defines common usage patterns/life cycles for AI agents, provides a very minimal LLM facade, allows defining dynamic agent workflows that can change at runtime, integrates with other key Jakarta EE APIs such as CDI, Validation, JSON Binding, Persistence, Messaging, and much, much more.