Reliable systems, from cloud to agents

Problems worth solving

• AI agents that work in a demo but fail in production. Tracing, evaluation gates, chaos testing and runtime guardrails that make agent behavior visible and controllable.
• Platforms that must scale without losing reliability. Multi-region, cloud-native architecture with observability and SRE practices built in from the start.
• No shared operating model for AI. Open standards for lifecycle management, governance and observability, developed through industry consensus.

Background

A DevOps Architect and Cloud & Agentic AI Solutions Architect with experience designing and operating large-scale distributed systems across cloud environments. Expertise in AWS, Kubernetes, Terraform and CI/CD, with a deep focus on reliability engineering, observability and multi-region scalability. Proven track record of building and operating resilient cloud-native microservices and serverless platforms, optimizing performance, and driving operational excellence across cross-functional teams.

A Microsoft MVP and Alibaba Cloud MVP, Pramod contributes to open-source engineering, IEEE standards development, technical research and developer communities. The work spans agentic AI observability, runtime governance, reliability and production operations.

Mission: bridge production engineering, agent reliability, open standards and knowledge sharing.