Operations | Monitoring | ITSM | DevOps | Cloud

Releasing new features is one of the highest-stakes moments in the software delivery life cycle. Even with CI/CD pipelines in place, plenty of things can still go wrong when a feature goes live for actual users. Most feature flagging tools operate in isolation from important observability tooling, forcing engineers to monitor changes across multiple disconnected systems to fully understand their impact. This slows down development and increases the chance of missing critical issues.

Today’s software organizations can’t make tradeoffs between speed and trust – you need both to succeed. But juggling them is tough. Moving too fast can lead to security vulnerabilities and compliance issues, while moving too slow means your competitors beat you to market. This tension creates friction that slows down every release, a problem that is rooted in your software pipeline.

As network teams constantly search for new ways to simplify and optimize their networks, it helps to hear from experts who have spent years at the center of these transformations. One of these voices is Fabio D’Avino, a specialist in Network as a Service (NaaS) with more than seven years of experience researching, designing, and building global network services across industries.

Retrieval-Augmented Generation (RAG) has transformed how applications interact with Large Language Models (LLMs). RAGs ground LLM responses in external knowledge, improves accuracy, and reduces hallucinations. But traditional RAG systems have a significant limitation: they only process text. Multimodal RAG addresses this limitation by processing and understanding multiple data types (text, images, and potentially audio).

The integration of Amazon Kiro and Harness’s MCP server enables developers to manage, troubleshoot, and optimize CI/CD pipelines directly from their IDE using natural language, dramatically reducing manual effort and accelerating software delivery from code generation to production.

Harness acquires Qwiet AI to power application security in the AI era, embedding reachability analysis to cut noise and prioritize real risks. By Sanjay Nagaraj, SVP Global Engineering, Harness; Co-founder and CTO, Traceable by Harness Today, I am excited to share that Harness has acquired Qwiet AI (formerly ShiftLeft), a leader in agentic AI-powered vulnerability detection and reachability analysis.

Are your Java applications running at their optimal performance, or is there room for improvement to make them faster and more efficient? With so many services depending on Java, keeping applications responsive and reliable is a core part of modern software engineering. This blog walks you through the leading Java Application Performance Monitoring (APM) tools, with a clear comparison to help you choose the right option for your needs.