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Build AI agents & agentic workflows with LangChain4J-CDI

Explore how to build autonomous, multi-agent AI workflows using LangChain4j-CDI with Jakarta EE and MicroProfile, leveraging CDI beans, annotations, and enterprise Java features for scalable AI applications. This presentation was presented at Jozi-JUG on 22 July 2026.

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J AK AR T A E E ¡ M I C R O P R O F I L E ¡ L AN G C H AI N 4 J LangChain4j-CDI Building Agentic AI Systems the CDI Way From a single autonomous agent to distributed, human-supervised multi-agent workflows using plain CDI beans, annotations, and Jakarta EE / MicroProfile.
Buhake Sindi • Founder: Sindi Technologies (Pty) Ltd • Enterprise Java expert (Java and Jakarta EE) • Developer of LangChain4J-CDI • Full member of the JCP • Jakarta EE / Microprofile Working Group member 3 3
T H E F O U N D AT I O N What Is LangChain4j? An idiomatic, open-source Java library for building LLM-powered applications on the JVM. 1 Unified API One consistent API across popular LLM providers and vector stores. 2 Tools & MCP Function calling, including native Model Context Protocol support. 3 Agents The langchain4j-agentic module for autonomous, multi-step workflows. 4 RAG Retrieval-Augmented Generation made straightforward to implement. </> Widely adopted as de-factor AI library for Java Java developers can seamlessly integrate to various AI LLM API and AI tools with consistently, without having to do various code changes to adopt other LLMs. This deck focuses on how Jakarta EE / MicroProfile developers consume LangChain4j through the LangChain4j-CDI extension — not the Quarkus integration. 03 LangChain4j-CDI Agentic
T H E E X T E N S I O N What Is LangChain4J-CDI? Seamless integration between LangChain4j and CDI (Contexts and Dependency Injection) — infuse any Jakarta EE application with AI, the same way you already build it. 1 Inject AI Services as Beans @RegisterAIService turns an interface into an injectable CDI bean. 2 Per-Topology Agent Annotations @RegisterSimpleAgent, @RegisterSequenceAgent, and more for multi-agent workflows. 3 Config-Driven LLM Setup Configure chat models via MicroProfile Config — or your own adapter. 4 Fault Tolerance Built In Add @Retry, @Timeout, @CircuitBreaker from MicroProfile Fault Tolerance. 5 Observability for Free Monitor AI operations with MicroProfile Telemetry / OpenTelemetry. 6 Runs on Any Jakarta EE Server Open Liberty, Payara, Helidon — CDI is portable across runtimes. 04 LangChain4j-CDI Agentic
AI E n g i n e e r i n g Building effective agents Anthropic released common agentic patterns to build agentic systems that can be deployed in production. ✓ Building block: Augmented LLM The basic building block of agentic systems is an LLM enhanced with augmentations such as retrieval, tools, and memory. ✓ Agentic workflows Prompt chaining, Routing, Parallelization, Orchestrator-workers, Evaluator-optimizer. ✓ Agents Agents augmented LLMs mature in key capabilities—understanding complex inputs, engaging in reasoning and planning, using tools reliably, and recovering from errors. It's used to define specific tasks using LLMs. ✓ Resource/Link https://www.anthropic.com/engineering/building-effective-agents 05 LangChain4j-CDI Agentic
Ag e n t i c S ys t e m LangChain4J Agentic LangChain4J Agentic provides a set of abstractions and utilities to help you build workflow and pure agentic AI applications, culminating in a Goal- Oriented Action Planning (GOAP) agentic system. 1 Building block: AI Agent One consistent API across popular LLM providers and vector stores. 2 Sharing states using AgenticScope Exchanging of data amongst AI agents. 3 Agentic workflow patterns Sequential, Loop, Parallel, Conditional. 4 Agentic and Non-AI patterns Supervisor, Human In The Loop. </> Enterprise-ready by design LangChain4j integrates seamlessly with enterprise Java frameworks like Jakarta EE, MicroProfile, Quarkus, and Spring Boot. This deck focuses on how Jakarta EE / MicroProfile developers consume LangChain4j through the LangChain4j-CDI extension — not the Quarkus integration. 03 LangChain4j-CDI Agentic
F o r e n t e r p r i s e J a v a d e v e l o p e r s Why CDI for Agentic AI? Agentic systems are just more moving parts. CDI is the tool Jakarta EE developers already use to manage moving parts. ✓ No new mental model Agents, tools, and workflows are CDI beans with familiar scopes and lifecycle — not a separate framework to learn. ✓ Declarative over imperative Compose multi-agent systems with annotations on interfaces, instead of hand-written builder/orchestration code. ✓ Enterprise features come as a free, added bonus Fault tolerance, telemetry, and configuration plug straight into agents via existing MicroProfile specs. ✓ Portable across runtimes Because it's standard CDI, the same code can run on Open Liberty, Payara, Helidon, and others. 05 LangChain4j-CDI Agentic
T H E AN N O T AT I O N C AT AL O G One Annotation per Agentic Topology Each agentic topology has a dedicated CDI stereotype annotation — pick the pattern, annotate the interface, and LangChain4j CDI generates the implementation. SIMPLE @RegisterSimpleAgent SEQUENCE @RegisterSequenceAgent PARALLEL @RegisterParallelAgent PARALLEL_MAPPER @RegisterParallelMapperAgen t CONDITIONAL @RegisterConditionalAgent LOOP @RegisterLoopAgent SUPERVISOR @RegisterSupervisorAgent PLANNER @RegisterPlannerAgent HUMAN IN THE LOOP @RegisterHumanInTheLoopAgen t A2A @RegisterA2AAgent MCP CLIENT @RegisterMcpClientAgent 11 topologies total Simple → Sequence → Parallel → Conditional → Loop → Supervisor → Planner → HITL → A2A → MCP 06 LangChain4j-CDI Agentic
C AS E S T U D Y The Liberty LangChain4j Workshop A free, hands-on workshop from OpenLiberty Builds AI-infused applications with Open Liberty and LangChain4j — starting from a simple chatbot and progressing to fully agentic, multi-agent systems. 1 Section 1 — AI-Infused Apps 10 steps: prompts, streaming, RAG, tools, MCP, guardrails, observability. 2 Section 2 — Agentic Workflows 7 steps: agents, workflows, supervisors, HITL, multimodal, A2A. ← This deck's focus. github.com/OpenLiberty/liberty-workshop-langchain4j Builds on and extends the Quarkus LangChain4j Workshop content, adapted for Jakarta EE / LangChain4j-CDI. The Scenario “Miles of Smiles”, a car rental company, needs to manage its fleet: recording feedback on returns, deciding on cleaning, maintenance, and — when damage is severe — whether to scrap, sell, donate, or keep a vehicle. This is exactly the kind of multi-step, judgment-based process that autonomous agents are built for. 07 LangChain4j-CDI Agentic
A K E Y D I S T I N C T I O N AI Services vs. AI Agents Feature AI Services AI Agents Purpose Answer user questions Perform autonomous tasks Interaction Reactive — responds to prompts Reactive & proactive — takes action Tool Usage Can call tools when needed Calls tools to accomplish goals Workflows Single-agent interactions Multi-agent collaboration Annotation @SystemMessage / @UserMessage One method per interface (@Agent) Use Cases Chatbots, Q&A, content generation Automation, decisions, orchestration 08 LangChain4j-CDI Agentic
B U I L D I N G B L O C K # 1 Getting started Simple imports — On your Maven POM. 09 LangChain4j-CDI Agentic
B U I L D I N G B L O C K # 2 The Simple Agent — @RegisterSimpleAgent Declared as an interface — no implementation needed. @SystemMessage defines the agent's role and decision logic. @UserMessage supplies per-call context via {{ template }} variables. toolNames wires in CDI-named tool beans the agent may call. The CleaningAgent decides: Given return feedback like “dog hair all over the back seat,” should this car be sent for cleaning — and if so, which services are needed? If not, it responds CLEANING_NOT_REQUIRED. CleaningAgent.java @RegisterSimpleAgent( name = "cleaning-agent", description = "Cleaning specialist...", chatModelName = "chat-model", toolNames = { "cleaning-tool" }, scope = ApplicationScoped.class ) public interface CleaningAgent { @SystemMessage(""" You handle intake for the cleaning department. Submit a request to the requestCleaning function if needed. Otherwise respond "CLEANING_NOT_REQUIRED". """) @UserMessage(""" Car: {{carMake}} {{carModel}} ({{carYear}}) Feedback: {{feedback}} """) String processCleaning( @V("carMake") String carMake, ... ); } No method body — LangChain4j CDI generates the implementation automatically. 09 LangChain4j-CDI Agentic
B U I L D I N G B L O C K # 3 Tools — Giving Agents the Power to Act CleaningTool.java @Named("cleaning-tool") @ApplicationScoped public class CleaningTool { @Tool("Requests a cleaning...") @Transactional public String requestCleaning( Integer carNumber, boolean interiorCleaning, boolean exteriorWash, ... ) { // update status, return summary } } @Named makes the tool a discoverable CDI bean by name. @Tool exposes the method to agents; its description tells the LLM when to use it. Parameters define exactly what information the agent must supply. Local or remote tools can run in-process or be reached over MCP. Tool Execution Flow Agent receives feedback LLM analyzes it LLM decides to call the tool (or not) LLM fills in parameters Tool executes & returns a result Agent responds 10 LangChain4j-CDI Agentic
B U I L D I N G B L O C K # 4 AgenticScope — Shared Context for Collaboration A shared map of key-value state that lets independent agents read what they need and write what they produce — without being tightly coupled to each other. Workflow Inputs AgenticScope shared state map Agent 1 reads / writes state Agent 2 reads / writes state Workflow Result Populated automatically from the workflow method's inputs. Updated automatically from each agent's outputKey after it runs. Also tracks the full invocation sequence for observability. 11 LangChain4j-CDI Agentic
C O M P O S I N G AG E N T S Sequential Workflows — @RegisterSequenceAgent CarProcessingWorkflow.java @RegisterSequenceAgent( name = "car-processing-workflow", subAgentNames = { "cleaning-agent", "car-condition-feedback-agent" }, outputKey = "carConditions" ) public interface CarProcessingWorkflow { CarConditions processCarReturn(...); @Output static CarConditions output( String carCondition, String cleaningAgentResult ) { // combine both agents' results return new CarConditions(...); } } Runs agents one after another — also called “prompt chaining.” subAgentNames lists the CDI bean names to execute, in order. outputKey stores each result in the AgenticScope for the next agent. @Output method combines multiple agents' results into one typed record. When to use Each agent needs the previous agent's output. Example: process feedback → update condition → notify. 12 LangChain4j-CDI Agentic
T H E P AT T E R N L I B R AR Y Four Fundamental Workflow Patterns Basic patterns nest inside one another to build sophisticated, deterministic agentic systems. SEQUENTIAL Agent 1 Agent 2 Agent 3 Runs one after another; each depends on the last. PARALLEL Start Agent 1 Agent 2 Agent 3 Runs simultaneously; faster, independent tasks. CONDITIONAL Condition? yes Agent A no Agent B Executes an agent only when its activation condition is met. LOOP Agent repeat until done Runs repeatedly until a stop condition or max-attempts is reached. 13 LangChain4j-CDI Agentic Continue Continue Continue? yes Done no
AU T O N O M O U S O R C H E S T R AT I O N The Supervisor Pattern — @RegisterSupervisorAgent Instead of hardcoded routing, an AI supervisor autonomously decides which sub-agents to invoke — and in what order — based on runtime context. FeedbackAnalysis Results FleetSupervisorAgent decides what to invoke PricingAgent estimate value DispositionAgent SCRAP/SELL/DONATE/KEEP MaintenanceAgent if needed CleaningAgent if needed Supervisor vs. Conditional Workflow Conditional: hardcoded if/else logic, fixed rules, simple to maintain by editing code. Supervisor: AI-driven decisions that adapt to context — update prompts, not code. 14 LangChain4j-CDI Agentic
R E U S E O V E R D U P L I C AT I O N One Agent, Many Tasks — @RegisterParallelMapperAgent Instead of three near-identical agents for cleaning, maintenance, and disposition, run one parameterized agent multiple times in parallel. FeedbackAnalysisWorkflow itemsKey = "tasks" FeedbackTask .cleaning() FeedbackTask .maintenance() FeedbackTask .disposition() FeedbackAnalysisAgent same agent, 3x in parallel FeedbackAnalysisResults cleaning / maintenance / disposition @SystemMessage("{{taskSystemInstructions}}") the instructions are data, not hardcoded — the same interface becomes a cleaning, maintenance, or disposition analyzer. itemsKey = "tasks" tells the framework which collection to iterate over, running the sub-agent once per item. @Output method converts the raw List<String> result into a structured, named record for downstream agents. 15 LangChain4j-CDI Agentic
S AF E T Y & C O N T R O L Human-in-the-Loop — @RegisterHumanInTheLoopAgent Any vehicle worth more than $15,000 must be reviewed by a human before disposition. The agent method itself pauses execution until a decision is made. PricingAgent value > $15,000 DispositionProposal Agent — creates SCRAP/SELL/etc. HumanApprovalAgent @HumanInTheLoop — blocks on Future.get() Human Reviewer Approve / Reject in UI Approved → execute SCRAP/SELL/DONATE. Rejected → fall back to Maintenance/Cleaning. Two-phase workflow — propose, then review, then execute. CompletableFuture bridges the paused agent and the REST approval endpoint. Timeout-safe defaults to REJECTED if no decision arrives in time. Full audit trail of who approved what, when, and why. 16 LangChain4j-CDI Agentic
D I S T R I B U T I O N Agent-to-Agent (A2A) — @RegisterA2AAgent Let a different team own and scale the pricing service independently — reachable from any application over an open protocol. Runtime 1 — Car Management (:9080) FleetSupervisorAgent PricingAgent @RegisterA2AAgent a2aServerUrl = "http://localhost:8888" DispositionAgent stays local — only pricing moves remote. JSON- RPC over HTTP Runtime 2 — Remote Pricing Service (:8888) AgentCard capabilities AgentExecutor handles tasks PricingAgent AI Service (local logic) Why A2A? Separation of concerns, independent scaling, reusable across client apps, technology-independent. Tasks vs. Messages Pricing uses a Task: a long-running job with a defined goal and tracked state. 17 LangChain4j-CDI Agentic
P U T T I N G I T AL L T O G E T H E R The Complete Miles of Smiles Architecture Car Return FeedbackAnalysis Parallel Mapper FleetSupervisorAgent autonomous orchestration PricingAgent remote via A2A Disposition Proposal → Approval HITL gate for high-value cars DispositionAgent local, low-value cars Maintenance / Cleaning routine agents CarConditionFeedback final summary Updated Fleet status + condition One workflow, five agentic patterns — sequential composition, parallel mapping, autonomous supervision, human oversight, and distributed A2A — all declared with CDI annotations. 18 LangChain4j-CDI Agentic
W R AP P I N G U P Key Takeaways ✓ CDI-native agents Agents, tools, and workflows are just managed CDI beans — familiar scopes, familiar lifecycle. ✓ Declarative composition One annotation per topology: simple, sequence, parallel, conditional, loop, supervisor, and more. ✓ Progressive sophistication Start with a single agent and grow into supervised, human- gated, distributed systems — same model throughout. ✓ Safety is built in Human-in-the-Loop and fault tolerance let you dial autonomy up or down as trust grows. ✓ Distribution when it pays off A2A lets specialized agents be owned by other teams and reused across applications. ✓ Portable across Jakarta EE Because it's standard CDI, it isn't locked to one runtime or one framework. 19 LangChain4j-CDI Agentic
T H AN K Y O U Go & Build Something Agentic Same CDI you already know. A whole new class of applications. LangChain4j-CDI github.com/langchain4j/langchain4j-cdi Liberty LangChain4j Workshop github.com/OpenLiberty/liberty-workshop-langchain4j LangChain4j-CDI Documentation langchain4j.github.io/langchain4j-cdi/ Built on the OpenLiberty “Miles of Smiles” case study — no Quarkus LangChain4j content used. LangChain4j Agents Docs docs.langchain4j.dev/tutorials/agents
Thank you Buhake Sindi X/Twitter: @EliteGentleman LinkedIn / Facebook / Instagram: Buhake Sindi