Operations | Monitoring | ITSM | DevOps | Cloud

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.

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.

The release of Anthropic Mythos and Project Glasswing marks an exciting and pivotal new chapter in software development. As the industry advances, the speed and economics of vulnerability exploitation have fundamentally shifted. What once took weeks of manual reconnaissance can now be scaled rapidly through automated models. However, this is not just a security problem to solve. It is a massive engineering opportunity to build cleaner, more robust systems.

What happens when your Infrastructure as Code management strategy works perfectly in dev, scales reasonably well in staging, and then quietly fractures across seventeen production workspaces because nobody documented which Terragrunt wrapper goes with which AWS account? You spend Friday afternoon reverse-engineering DRY patterns that made sense six months ago, wondering why your team is managing three different IaC execution engines with four incompatible workflow philosophies.

If you've ever run an ALTER TABLE on a busy MySQL table in production, you know the feeling. The change is small. The risk isn't. Long-running table locks, queued writes, application timeouts, replication lag, a five-minute migration that turns into a half-hour incident review. We're shipping an integration that takes that anxiety out of the loop. Harness Database DevOps now supports Percona Toolkit for MySQL as part of Liquibase-based schema management.

Most development teams today build everything around Git, and deploy with GitOps principles. Code sits in version controlled environments, changes go through PRs, and deployments are handled through modern CI/CD. That part is pretty standard at this point, especially when using a modern DevOps platform like Harness.

Application security testing tools promise coverage and accuracy, but teams often struggle just to get started. One of the biggest friction points in dynamic application security testing is configuring authentication correctly so a scanner can even access a target application, let alone API endpoints that power the functionality. Whether it’s API keys, bearer tokens, or custom auth flows, setting up authentication for scans frequently requires trial-and-error and engineering support.