Seetaram Rayarao
VP, Senior Lead Engineer
Middletown, Delaware, United States
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Seetaram Rayarao is a Vice President and Senior Lead Engineer at JP Morgan Chase with over 18 years of experience driving innovation in cloud computing, AI, and microservices. He leads transformative projects, including the LLM Suite, integrating Generative AI frameworks such as LangChain and LangGraph with enterprise cloud solutions to deliver scalable, AI-driven applications. A Delaware AWS Community Leader (AWS Builder), Seetaram is an invited speaker at the New York AI Summit, where he presented on real-world enterprise adoption of Generative AI and agent-based systems, sharing practical lessons on scaling, governance, and measurable business impact. He is also a speaker at AI4, contributing advanced insights on production-ready AI architectures, RAG systems, and evaluation frameworks used in regulated financial environments.
Area of Expertise
Topics
Building Secure, Scalable, and Reliable Agentic AI Systems
The Model Context Protocol (MCP) is rapidly emerging as a foundational standard for building agentic AI systems that interact with tools, data sources, and services in a consistent and interoperable way. However, adopting MCP effectively requires more than basic integration—it demands thoughtful design choices around security, scalability, observability, and reliability.
This session presents practical best practices for implementing MCP in real-world agentic AI applications. It covers how to structure MCP servers and tools, manage context boundaries, handle permissions and sensitive data, and design resilient agent workflows. The talk also explores patterns for prompt engineering, tool invocation, state management, and error handling when using MCP in cloud-native environments.
Attendees will leave with concrete guidance on how to use MCP to move from experimental agents to production-ready systems that are secure, maintainable, and scalable.
PRO Session: Engineering Production-Ready GenAI and Agentic Systems at Scale
Generative AI is no longer just experimental. Enterprises today are building production-ready agentic systems that operate reliably, securely, and efficiently at scale. But how do you turn prototypes into robust, production-grade AI applications?
In this session, you’ll learn how to:
* Design resilient AI platforms that integrate reasoning, retrieval, and tool orchestration.
* Automate infrastructure and deployment using Terraform and GitHub Actions.
* Leverage foundation models from Bedrock, OpenAI, and Perplexity for scalable AI workflows.
* Scale across cloud environments like AWS, Azure while ensuring reliability and resilience.
We’ll walk through real-world strategies to deploy and operate large-scale agentic applications, showing how to make GenAI systems production-ready, maintainable, and highly available. By the end, you’ll know how to take your AI projects from proof-of-concept to enterprise-scale deployment.
Orchestrating Multi-Agent AI solutions with OpenAI in Azure: Real-World Architectures and MCP
Discover how to leverage Azure OpenAI Service to build scalable, secure, and orchestrated multi-agent AI solutions using the Microsoft Multi-Agent Control Plane (MCP). This session will explore architectures and patterns for deploying composable agent frameworks (such as LangChain and LangGraph) atop Azure while addressing challenges in security, observability, and responsible AI. Real enterprise use cases from regulated industries will be highlighted, showcasing practical strategies for agent orchestration, robust monitoring, prompt chaining, and seamless integration with cloud-native microservices. Walk away with blueprints you can adopt in your own Azure deployments.
Getting Started with Building AI Agents: A Step-by-Step Guide
Dive into the exciting world of AI by building intelligent agents powered by cutting-edge technologies like LangChain and LangGraph. This guide provides a comprehensive introduction to creating robust, scalable, and context-aware AI agents for various applications. Learn how to:
Utilize LangChain for seamless integration of language models into your workflows.
Leverage LangGraph to design dynamic, modular, and interactive workflows for AI systems.
Build agents capable of reasoning, decision-making, and task automation across industries.
Integrate external data sources, APIs, and tools for enhanced functionality.
Optimize performance, scalability, and maintainability for production-ready solutions.
Whether you're a beginner or a seasoned developer, this guide will empower you with the knowledge and practical tools to kickstart your journey in building impactful AI agents.
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