Operations | Monitoring | ITSM | DevOps | Cloud

A customer pings support: “I tried to check out twice this morning and got a 500 each time, but it works fine for everyone else.” The session ID is in the email. You have full request/response capture in your environment, you have Datadog Synthetics already running browser checks against the same flow, and you still spend the next two hours grepping logs because none of those tools let you say “show me just this user’s requests, in order, and re-run them.”

Key takeaway: Real cloud strategy isn't about running the same workload everywhere at once; it’s about the freedom to move when you need to. By standardizing the unified configuration file, Upsun enables true cloud optionality, moving provider migration from a re-architect project to a data move project.

The question for enterprise AI in 2026 is no longer just which model. It’s which harness. An agent harness is the system around the model. It decides what the agent remembers, what context it sees, what tools it can call, what it is allowed to do, and what happens when it is wrong. The model provides intelligence. The harness provides control. This is where the real engineering is happening.

TLDR: Today, Harness is introducing the Harness Cursor Plugin, bringing the power of the Harness AI-native software delivery platform directly into Cursor. This integration, along with the Harness Secure AI Coding hook for Cursor, allows developers and AI agents to move from code changes to vulnerability detection, CI/CD execution, security validation, approvals, deployments, and operational insight without leaving the editor. AI has completely changed how we write code.

Not every incident alert needs the same kind of response. One incident may need to wake someone up right away. Another may simply need to be picked up when the team starts work in the morning. Without a clear way to tell them apart, every incident feels equally urgent. That usually adds noise and makes incident response decisions harder than they need to be. This is where two questions help: In this guide, we’ll discuss what those questions mean and the four combinations that follow.

LocalStack lets you run SQS, Lambda, and S3 locally in Docker — but there's a hidden trap: OpenTelemetry's default AWS propagator doesn't work with free LocalStack. Here's how to set up end-to-end local testing with working trace propagation. Prathamesh works as an evangelist at Last9, runs SRE stories - where SRE and DevOps folks share their stories, and maintains o11y.wiki - a glossary of all terms related to observability.

Training a model in a notebook is easy. What breaks teams is the step after, serving it reliably without haemorrhaging cloud budget or burying your SREs in YAML. The common trap: picking a platform that handles the model but not the surrounding stack. An AI deployment platform should orchestrate the full application graph (inference endpoints, vector databases, caching layers, and frontends) inside a single VPC, with GPU autoscaling that doesn't require a dedicated platform engineer to babysit.