Harness AI + MCP server: A Single Prompt to Accelerate the Software Development Lifecycle

Pipeline Creation: Using a single prompt in the IDE, a CI/CD pipeline is created and triggered via the agent connected to the Harness MCP server.
Failure Diagnosis and Fix: When the pipeline fails, the agent is used to diagnose the issue (a failed dependency) and propose a fix, which is then committed, pushed, and the pipeline re-triggered to succeed.
Deployment: After a successful build, the artifact is deployed into a Kubernetes cluster.
Incident Response: An alert from an EKS cluster automatically creates an incident, and the AI correlates information from sources like Slack and a team call to surface the probable root cause, identifying the last deployment and pull request.
Automated Rollback: The service is rolled back using a simple prompt to the agent, reverting to a known version.
Pipeline Hardening:
AI-driven recommendations for fault experiments are used to add a CPU stress test to the pipeline for resilience testing.
The deployment strategy is updated from a simple rolling deployment to a canary deployment with continuous verification (CV) using the DevOps agent inside Harness. CV uses machine learning and AI to analyze data, automatically detect anomalies, and trigger a rollback before users are impacted.
Ultimately, from a single prompt in the IDE, the application was built, deployed, an incident was triaged, the service was rolled back, and the pipeline was hardened against future failures.