Operations | Monitoring | ITSM | DevOps | Cloud

My wife and I spent hours this summer at home digging in the dirt. We planted new shrubs and perennials and created a small vegetable garden. We spread many square yards of fresh topsoil and grass seed over areas of lawn that needed rejuvenation. It turns out, I should have done all that landscaping with a FinOps leader’s mindset — before my water bill tripled when I wasn’t looking.

Email automation allows you to send emails automatically based on certain triggers or schedules, so you don’t have to click the Send button every time. This includes things like welcome messages, drip campaigns, and regular newsletters. In this tutorial, you will create a simple system that automatically welcomes new subscribers and sends them updates about technology, all with the help of AI.

As a platform integrator, we get a unique look at the tools our customers adopt every day. Of all the shifts I’ve seen, none has been as rapid as the adoption of coding assistants. The conversation has quickly gone from ‘is this tool really going to drive value?’ to ‘how quickly can we roll this out?’ No one can doubt the immense value these tools provide in shipping code faster.

AI-powered coding alone isn’t enough. True software delivery velocity requires end-to-end automation and intelligent governance across the entire lifecycle. Harness enables organizations to escape the AI Velocity Paradox by unifying speed, safety, and resilience, turning rapid development into a sustainable competitive advantage. The widespread adoption of AI coding assistants is transforming software engineering.

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.

Before we talk about AI-native monitoring, let’s take a quick step back to make sure everyone is on the same page. In software engineering, monitoring is the continuous collection and analysis of data about a system’s health, performance, and behavior. Tools like Scout Monitoring, Datadog, and New Relic traditionally track server uptime, request latency, error rates, and database performance.