Operations | Monitoring | ITSM | DevOps | Cloud

For the past year, I've been hearing a version of the same thing from engineering leaders: AI tools are working, productivity is up, the business case is there. And yet, something about the picture still feels incomplete. So we decided to go find out how widespread that feeling actually is. We surveyed 700 engineers and managers across five countries, and published the results in the State of Engineering Excellence 2026.

Effective disaster recovery testing follows a clear three-phase lifecycle: plan, execute, and review. Most DR programs fail not because of missing tools, but because of untested runbooks and unclear ownership. Platforms like Harness Resilience Testing bring chaos, load, and DR testing into one pipeline so teams can catch risks before they become incidents. Most organizations don't fail at disaster recovery because they lack technology.

The first quarter of 2026 introduces AI-powered continuous verification that eliminates configuration overhead, expanded deployment platform support including Azure Container Apps and enhanced Windows capabilities, and GitOps workflow improvements that align with how teams actually ship software.

Enterprise releases spanning multiple services, teams, and environments demand more than spreadsheets and manual coordination. Harness Release Orchestration provides a unified framework for modeling, automating, and tracking complex releases with complete visibility from planning through production deployment.

The first quarter of 2026 introduces eight major pipeline orchestration enhancements that accelerate development, simplify validation, and strengthen governance. Execute pipelines from Git tags for immutable versioning, leverage AI to author OPA policies without Rego expertise, and gain complete visibility into queued pipelines across your account.

Application security & engineering teams are under pressure to move faster, cover more, and reduce the operational drag that often comes with security testing. But in practice, two problems keep slowing teams down and adding friction.

AWS re:Invent 2025 made one thing very clear: enterprise interest in AI is no longer theoretical. The conversation has moved beyond curiosity. Teams are actively experimenting, leaders are looking for production-ready use cases, and engineering organizations are trying to figure out where AI can create real leverage across software delivery, security, platform engineering, and operations.

It’s becoming increasingly clear that AI-generated code can create real challenges once it reaches production. At Harness, we’ve been focused on innovating fast and solving those problems, so teams can move quickly without sacrificing reliability. In the past 30 days, we delivered 70+ new features.

‍Summary: Google Cloud Next ’26 focused on the future of software delivery, emphasizing that AI, platform consolidation, and an urgent push toward efficiency are reshaping the Software Development Life Cycle (SDLC). The key takeaway from the event was that organizations are moving from AI experimentation to operationalization, actively consolidating fragmented tools onto end-to-end platforms that embed AI for control, intelligence, and speed. ‍

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.