Microservices assume predictable callers. AI agents break this with non-deterministic calls, fan-out, and retries. Here are 5 core assumption breaks and fixes.
Demonstrates how to expose Spring Boot metrics with Prometheus and build Grafana dashboards to track memory usage and error rates for production-grade Java services.
Decouple heavy processing with Spring Boot, Kafka, and WebSockets: AI consumers analyze events asynchronously, while WebSockets deliver real-time insights to users.
Distributed AI systems fail faster than humans can respond, making traditional response insufficient. Self-healing systems use telemetry and automation to recover early.
Microservices add flexibility and scalability but increase complexity. Learn key challenges in observability, DevOps, and data management when moving from monoliths.
Apereo CAS is one of the largest open-source Spring Boot applications in production. Learn about seven battle-tested patterns from its codebase that will improve yours.
Learn to transform Spring Boot REST APIs into an event-driven architecture by utilizing Kafka, RabbitMQ, or NATS to enhance scalability, resilience, and responsiveness.
Retry transient failures, route poison messages to a DLQ, and deduplicate with a DB table three layers that turn a fragile Kafka consumer into a fault tolerant one.
PDF chatbot demo comparing LLM+API vs MCP: direct calls are simple for one app; MCP adds a server layer for tool discovery, reuse, and standardization.
In this article, you will learn how to log incoming requests in Spring Boot, using the class CommonsRequestLoggingFilter, through some simple configuration steps.
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