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Key Takeaway: The Harness MCP Server is now in the official Claude Connectors Directory. Developers using Claude can now discover and connect to Harness, gaining structured, real-time access to their pipelines, deployments, approvals, and delivery workflows. What makes this different from a typical API integration is what's underneath: the Harness Software Delivery Knowledge Graph, which gives Claude the context it needs to make decisions that are accurate, fast, and safe. ‍

AI spend is going parabolic, and the labels on the bill (OpenAI, Anthropic, Gemini) are about all a CXO gets to work with. The hard part of tying that spend to outcomes is structural. A major portion of AI spend isn’t COGS. It’s the spend on coding agents producing the software, the spend on building marketing content, the spend on custom sales tooling, the spend on Intercom agents and Sybill analysis.

CloudZero continues to ship new features and enhancements at a fast pace. We’re publishing these product update posts to keep you up to date on our velocity and how we’re adding customer value.

In AWS environments, a data perimeter is a set of preventative controls that help ensure that your trusted cloud identities (principals or AWS services acting on your behalf) are accessing trusted resources from authorized networks. You can apply these controls at various levels of your infrastructure, such as per resource or across all resources in your AWS account.

Spark jobs only get more expensive and harder to debug as they scale. It’s a problem we’ve run into ourselves. Our Referential Data Platform team builds and maintains the knowledge graph that maps relationships between customers’ observability entities. ServiceQueryEdge is at the center of that graph, mapping service entities to their associated metric and log queries.

Azure Managed Redis is a Microsoft first-party, fully managed in-memory data store, replacing Azure Cache for Redis tiers. It includes Redis Enterprise features such as RediSearch for vector search and full-text search, in addition to RedisJSON, RedisTimeSeries, and Active Geo-Replication. As Azure Cache for Redis reaches end of life, more teams are planning migrations to Azure Managed Redis in search of better performance, lower cost, and modern capabilities for AI and real-time workloads.

Every application starts the same way: One server. One database. One optimistic engineer saying: “We’ll scale later.” And honestly? That’s usually the right call. Premature scaling is how perfectly normal applications end up with: But eventually, growth happens. Traffic increases. Queries slow down. Deployments get riskier. Your infrastructure starts making unfamiliar noises. This is where scaling enters the picture. Not scaling for conference talks.

Brandon Woods, Keysight Technologies, on the Difference Between a DEX-Powered IT Agent and a Generic AI Assistant “Spark’s real power is the Nexthink data behind it.

This blog helps IT leaders use the Forrester Consulting TEI study as a practical framework for evaluating Edwin AI in their own environments. A Total Economic Impact study is useful for one, critical reason: it takes a broad technology claim and turns it into a financial and operational framework. That matters in AI for IT operations because the market is crowded with claims. Every platform says it reduces noise. Every platform says it improves efficiency. Every platform says it helps teams move faster.

Some support cases are straightforward. Others lead deep into legacy code, where a single logic bug can quietly turn a routine command into a major performance problem. This series looks at how Canonical Support and Sustaining Engineering work together to investigate, patch, and upstream difficult issues that standard troubleshooting alone cannot solve.