Microservices assume predictable callers. AI agents break this with non-deterministic calls, fan-out, and retries. Here are 5 core assumption breaks and fixes.
AI is transforming multi-cloud integration with real-time, decentralized, secure systems — improving compliance, APIs, and scalability across industries.
Observability costs spiral when teams optimize for visibility, not cost. Fix it by making spend visible, sampling aggressively, and cutting low-value data.
AppSec focuses only on code, leaving AI supply chains exposed. Effective security embeds AI checks into workflows, scanning PRs and AI components continuously.
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.
CI/CD pipelines are essential, but they carry risks if not designed correctly. This post discusses common security mistakes and shares practices to avoid them.
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.
AI-driven development expands attack surfaces; this article shows how continuous security, zero trust, and runtime enforcement scale DevSecOps in AI pipelines.
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