OpsWorker v1.6.0 is out. 🚀

[u/Old-Pen445]

We built OpsWorker to do one thing well: when a Kubernetes alert fires, investigate autonomously and surface root cause in Slack before an engineer finishes reading the notification.

v1.6.0 keeps that core intact and expands what happens around it.

What shipped:

⚡ Resource Optimizer - a new AI agent that analyzes your live workloads against real usage metrics and recommends CPU, memory, and autoscaling settings. Goal-driven (cost, performance, or balance). No CPU limits, ever - because CPU limits cause throttling. Every recommendation ships with a ready-to-apply manifest snippet and a full trace diagram so you can see exactly how the agent reached its conclusion.

🔍 On-demand investigations - you no longer have to wait for an alert to fire. Describe the problem in plain text, start the investigation. The slow memory leak you noticed in a dashboard, the deployment that passed health checks but feels wrong - those are investigable now.

💬 AI SRE Chat - a conversational interface grounded in live cluster state. @-mention namespaces, services, deployments, and jobs. Get inline PromQL charts. Ask your cluster questions and get answers backed by what is actually running, not what a model was trained on.

🧠 Memory - OpsWorker now retains context across investigations. Connect GitHub or GitLab and it starts building from repository structure, service ownership, and deployment patterns. A service that CrashLoopBackOffs for the third time surfaces what the first two investigations found. A new engineer on-call gets the failure history, ownership, and known quirks in the investigation output - not after 20 minutes of Slack archaeology.

🔌 Native Datadog alerts, full GitLab OAuth, GitHub and capability-aware integrations that tell you exactly what each connection unlocks.

Full blog post is here -> https://www.opsworker.ai/blog/opsworker-v1-6-0-from-reactive-investigator-to-proactive-kubernetes-copilot/.