Spacelift Expands Observability Reach to OpenTofu

Spacelift this week at the KubeCon + CloudNativeCon North America 2026 conference extended the reach of its observability platform to OpenTofu, an open source infrastructure-as-code (IaC) tool that is being advanced under the auspices of the Cloud Native Computing Foundation (CNCF). Dimitri Vlachos, chief marketing officer for Spacelift, said that as OpenTofu continues to gain…

Mike Vizard Nov 13, 2026

Cloud-Native

Diagrid Updates Dapr Runtime for the Agentic AI Era

Diagrid at the KubeCon + CloudNativeCon North America 2026 conference has made available an update to the open source Distributed Application Runtime (Dapr) that makes it possible to deploy artificial intelligence (AI) agents in a way that consumes infrastructure resources more efficiently. The Dapr framework is based on Actors that are used to build stateful…

Mike Vizard Nov 13, 2026

Local DevOps

AWS Tool Enables Developers to Build Cloud Apps on Local Machines

Amazon Web Services (AWS) today added an emulation capability that makes it simpler for application developers to build applications on a local machine before deploying them in the cloud. Tom “Spot” Callaway, principal open source software strategist for AWS, said the open source AWS Emulator project makes it possible to build and test applications on…

Mike Vizard Nov 11, 2026

Observability, Monitoring and Analytics

ControlTheory Adds Traces and Metrics to AI Platform for Analyzing Telemetry Data

ControlTheory today at the KubeCon + CloudNativeCon North America 2026 conference added support for metrics and traces to a platform that analyzes telemetry data at the point where it is collected. Company CEO Bob Quillin said version 2.0 of the Dstl8 platform is now able to analyze logs, traces and metrics in a way that…

Mike Vizard Nov 11, 2026

Blogs

Cloudsmith Extends Artifact Registry Reach to AI Skills

Cloudsmith this week at the KubeCon + CloudNativeCon North America 2026 conference unveiled a registry for artificial intelligence (AI) skills that makes it simpler for software engineers to expose a set of reusable instructions, workflows, and resources that teach an AI coding agent how to better perform a specific task. James Matchett, head of security…

Mike Vizard Nov 11, 2026

Featured Reports

DevOps.com Research

The State of Observability 2026: Bigger Budgets, Higher Stakes and the Rise of the Agent

Observability budgets are climbing fast: a Futurum Group survey of 628 enterprise IT leaders reported by DevOps.com found 36% plan to spend more than $1 million on observability in 2026, with 7% planning to spend more than $5 million. This report draws on Futurum Group research, Enterprise Technology Research (ETR) spending-intention data, practitioner reporting from DevOps.com, Cloud Native Now and PlatformEngineering.com, and executive interviews and webinars from Techstrong TV and Techstrong Learning. Five findings stand out: spending is moving up-market; cost control is the central operational problem; no enterprise has a truly unified platform; AI observability has become a buying priority before agents are widely deployed; and platform engineering is becoming the delivery vehicle for observability.

Techstrong EditorialUpdated November 5, 202616 min read

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DevOps.com Research

The State of AI Coding in DevOps Workflows

Coding using artificial intelligence (AI) tools is no longer an experiment for DevOps teams. It is the new operational reality. Futurum Research, for example, finds that 76.6% of organizations actively use AI in development workflows and another 20.4% are evaluating it. The ETR arm of the Futurum Group, meanwhile, is reporting that 93% of AI coding tool users report measurable productivity gains, which ETR calls the strongest signal of tangible return from investments in AI. This report draws on Futurum Group research, Enterprise Technology Research (ETR) data, practitioner reporting from DevOps.com, Cloud Native Now and PlatformEngineering.com, and executive interviews and webinars from Techstrong TV and Techstrong Learning. Four findings stand out: AI coding adoption has reached critical mass; the bottleneck has moved from writing code to reviewing and verifying it; coding agents are privileged software that widen the supply chain; and platform engineering is becoming the control plane for AI-generated code.

Techstrong EditorialUpdated November 5, 202616 min read

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