Operations | Monitoring | ITSM | DevOps | Cloud

{unscripted} AI Verification and Rollback

Our first AI/ML capability, Continuous Verification, made Harness the first Continuous Delivery tool to understand observability telemetry and trigger rollbacks when deployments caused trouble. We knew we could do more to eliminate the friction involved in its setup. Deploying with confidence shouldn't require a coordination meeting between DevOps, SREs, and developers just to configure the right health checks. That’s why we’re introducing the next generation: AI Verification and Rollback.

{unscripted} AI in Chaos Engineering

Harness AI enhances your chaos engineering capabilities by leveraging artificial intelligence to automate and optimize reliability testing and analysis. One of the challenges of scaling up the Chaos Engineering practice within the organization is skilling up the users to create or run chaos experiments and to come up with solutions to mitigate the risks that are identified during the chaos experiment execution. The Chaos Engineering module comes with an AI Agent called "AI Reliability Agent" that helps in these aspects.

Streamline Software Delivery Right From Your IDE with Amazon Kiro and Harness

The integration of Amazon Kiro and Harness’s MCP server enables developers to manage, troubleshoot, and optimize CI/CD pipelines directly from their IDE using natural language, dramatically reducing manual effort and accelerating software delivery from code generation to production.

Grafana Labs Co-founder Woods: Market maturity, OpenTelemetry, and AI are reshaping observability

As organizations navigate increasingly complex tech environments, unified observability practices have become essential. That was one of the main takeaways from Grafana Labs Co-founder Anthony Woods’ recent appearance on “Tech Keys by by Mercari India,” a podcast hosted by Vaibhav Khurana, Head of Platform Engineering at Mercari India.

How To Tag AI Cloud Spend: A Practical Framework For FinOps Teams

The world of cloud costs is always evolving, and AI spend is quickly becoming one of the most unpredictable and confusing cost drivers. As more organizations integrate generative AI into their products, FinOps teams are struggling to account for — and control — these new, often mind-boggling cost streams. In fact, 44% of engineering professionals say improving AI explainability is a top priority in AI budgeting, according to CloudZero’s State Of AI Costs In 2025 report.

AI-Powered Chaos Engineering with Harness MCP Server and Cursor

The Harness MCP Server integration with Cursor transforms chaos engineering from a complex, specialized discipline into an accessible, conversational workflow that any developer can leverage directly within their AI-powered IDE. By combining natural language prompts with comprehensive resilience testing tools, teams can discover, execute, and analyze chaos experiments without vendor-specific expertise, democratizing system reliability across DevOps, QA, and SRE functions.

How Nexus BMS Uses Time Series and AI to Power Smarter Buildings

Monitoring equipment isn’t enough for today’s smart buildings; true value comes from being able to predict issues, optimize performance, and take action automatically. Traditional building management systems often fall short, limited to dashboards and alarms that only notify you of an issue after the fact. With the rise of open source hardware, modern databases, and AI-driven diagnostics, facilities can now move from reactive to proactive management.

The Compounding Returns of Blending Agentic Execution with Generative Creativity

— Jensen Huang, NVIDIA GTC 2025 Enterprise AI strategies have rapidly evolved, with substantial investments in Generative AI technologies delivering significant but limited business value. While Generative AI excels at content creation and information synthesis, its fundamentally reactive nature constrains its ability to drive autonomous business outcomes.