🔸 TL;DR

Jakarta EE developers get a familiar annotation-driven model: trigger an agent, make an AI-assisted decision, execute actions, produce an outcome, and recover from failures.

Simplified M1 excerpts below; application-specific types and services are omitted.

🔸 TRUTH

The first Jakarta Agentic AI 1.0 milestone is out! 🎉 It offers an initial specification for community feedback while the implementation, TCK, and final v1 release move closer.

🔸 DEFINITION

Jakarta Agentic AI is a vendor-neutral API for building, deploying, and running AI agents on Jakarta EE. It standardizes agent lifecycles, workflows, CDI integration, and a lightweight LLM facade; not the underlying LLM providers.

1️⃣ DEFINE AN AGENT WORKFLOW

@Agent creates a CDI bean. @Trigger starts the workflow, @Decision evaluates it, and @Action performs the work.

2️⃣ GET A TYPED LLM RESPONSE

The LLM facade can deserialize JSON into a domain type through Jakarta JSON Binding, avoiding manual text parsing.

3️⃣ RECOVER FROM LLM FAILURE

A matching @HandleException method can compensate for workflow failures. Here, an unavailable model routes the transaction to manual review.

🔸 TAKEAWAYS

▪️ Jakarta annotations make agent workflows readable.

▪️ CDI connects agents, LLMs, and enterprise services.

▪️ Typed responses reduce parsing boilerplate.

▪️ Transactions and recovery remain part of the model.

▪️ M1 is not the final 1.0 API, so details may evolve.

A promising step toward portable agentic AI for Jakarta EE. 🚀

#JakartaEE #JakartaAgenticAI #Java #AI #AgenticAI #LLM #CDI #EnterpriseJava

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