Agentic AI is moving beyond RAG into systems that plan, execute, and interact with tools across enterprise environments. However, productionizing these systems introduces challenges around reliability, observability, and governance.

This session presents a cloud-native architecture for building Agentic AI systems on Kubernetes. We will cover multi-agent orchestration, tool execution layers (APIs, SQL), memory management, and integration with data platforms. The talk also introduces evaluation-driven development practices such as shadow testing, confidence scoring, and regression validation for LLM outputs.

In addition, we will explore guardrails for safe execution and observability patterns using tracing, metrics, and feedback loops to monitor agent behavior.

Through real-world case studies in AI-driven automation and analytics systems, attendees will gain practical design patterns to build scalable, reliable, and production-ready AI systems using cloud-native principles.