Operations | Monitoring | ITSM | DevOps | Cloud

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.

For organizations managing complex release trains, especially in regulated industries, Harness Release Orchestration provides the governance and visibility needed to deliver with confidence. And with AI-powered assistance, you can even model new release processes using natural language, which Harness then translates into structured YAML.

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.

We're making Internal Developer Platforms (IDPs) more accessible with a natural language assistant. Developers can ask questions like, "What are the failing checks for my service's scorecard?" or "Who is the owner of a service?" to find metadata instantly. The agent also bridges the gap to action by suggesting and executing self-service workflows, like creating a new repo or onboarding a new engineer. It can even assist in generating new workflows, turning complex processes into simple conversational tasks.

Many software engineers are experts in application code but not in the nuances of creating a production-ready delivery pipeline. Architect Mode acts as a seasoned DevOps expert, engaging the user in a conversation to design a pipeline that incorporates organizational best practices for security, quality, and compliance from the very beginning. It’s like having a personal DevOps architect as a partner.

Nothing drains developer productivity like codebase maintenance. The endless cycle of dependency upgrades, bug fixes, refactoring, and paying down technical debt is tedious, error-prone work that pulls engineers away from building new features. Harness Autonomous Code Maintenance (ACM) turns these manual chores into automated, intent-driven workflows. Developers can now state their intent in plain English, with prompts like, "Upgrade the front end from React 15.6 to 16.4". From there, the Harness AI agent drives the workflow.

In this upcoming Grafana Campfire Community Call, we will talk about the Grafana UI Extensions, where we will discuss how the Framework enables plugins to interoperate by adding links, components, or functions into defined places (extension points) in Grafana. We will talk about (but not limited to):What are UI Extensions and where you find the resourcesHow it can be leveraged to deliver fun new featuresAdding custom actions, links, and components to various parts in UI and much more.....

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.