This project is a simple Agentic AI Proof of Concept built using Spring Boot + Spring AI + OpenRouter (LLM) . It demonstrates how an AI Agent can investigate a problem step-by-step (like a human engineer) instead of following a fixed hardcoded workflow. 🎯 What Problem Does It Solve? Given a goal like: “Why is my Order API slow?” The agent will: Check system metrics Check database performance Check Kafka lag (optional) Correlate results Find the root cause Stop automatically 🤖 What Is “Agentic AI” Here? This is NOT a chatbot . This is: A state machine where an LLM is used only to decide the next step . The LLM: Does NOT access DB Does NOT access metrics Does NOT execute anything It only says: “Check metrics” “Now check DB” “Now we are done” All real work is done by Java code (tools) . 🏗️ Architecture (Simple View) User Goal ↓ Agent Loop ↓ LLM decides next action ↓ Java executes tool (DB / Metrics / Kafka) ↓ Result stored in memory ↓ Loop again until root ...
Introduction to Reactive Programming in Java with Project Reactor we will go through below: 1. Introduction to Reactive Programming What is reactive programming? Key principles of reactive systems: Responsive : Systems should respond in a timely manner. Resilient : Systems should be fault-tolerant. Elastic : Systems should scale as needed. Message-driven : Systems should use asynchronous messaging. Comparison between imperative programming and reactive programming. 2. Understanding Reactive Streams Publisher , Subscriber , Subscription , and Processor interfaces. The four key signals: onNext() , onComplete() , onError() , and onSubscribe() . Backpressure handling in reactive systems. 3. Introduction to Project Reactor What is Project Reactor? Key classes: Mono and Flux . Mono : Represents 0 or 1 item. Flux : Represents 0 to N items. Non-blocking nature and how it helps in building scalable systems. 4. Building a Reactive Application with Project Reactor Demonstrating how to use Mono ...