From Prototype to Production: Building Autonomous AI Agent Swarms on Serverless Cloud Infrastructure

The future of enterprise AI isn't just about smarter models—it's about intelligent systems that can scale, adapt, and collaborate autonomously in the cloud. This session reveals how to architect production-ready AI agent ecosystems using serverless computing principles, where thousands of specialized agents can spawn, communicate, and coordinate without human intervention. We'll explore the revolutionary Model Context Protocol (MCP) that enables secure agent-to-agent communication while maintaining enterprise security boundaries. Through live demonstrations and real production metrics, you'll discover how stateless agent architectures achieve 10x better scalability than traditional approaches, handling 10,000+ concurrent AI conversations with sub-100ms response times. Key topics include: serverless agent orchestration patterns, autonomous scaling strategies, inter-agent communication protocols, and the economic advantages of pay-per-inference pricing models. We'll also dive into emerging patterns like agent specialization, dynamic tool discovery, and self-healing AI systems that automatically recover from failures.
Who is this presentation for?
Cloud architects designing AI-powered applications Senior software engineers implementing LLM-based systems Technical leads responsible for scaling AI from prototype to production DevOps engineers managing AI workloads in cloud environments CTOs and engineering managers evaluating AI infrastructure strategies
Prerequisite knowledge:
Basic understanding of cloud computing concepts (containers, APIs, microservices) Familiarity with serverless computing principles (AWS Lambda, Azure Functions, or Google Cloud Functions) General knowledge of AI/ML concepts and large language models Experience with distributed systems and scalability challenges Understanding of enterprise security and authentication patterns
What you'll learn?
How to architect stateless AI agent systems that scale linearly with demand Implementation patterns for the Model Context Protocol (MCP) for secure agent communication Serverless deployment strategies that reduce AI infrastructure costs by 40-60% Production-tested patterns for handling thousands of concurrent AI conversations Autonomous scaling techniques that eliminate manual capacity planning Security models for multi-tenant AI systems with enterprise-grade authentication Performance optimization strategies achieving sub-100ms response times at scale Economic models and cost optimization techniques for production AI workloads

Sowjanya Pandruju is a Cloud Application Architect at AWS with over 13 years of software development experience, specializing in cloud-native development, AI/ML integration, serverless computing, and event-driven architecture. As a Senior Staff Engineer and Architect, she has led large-scale cloud migrations from on-premises systems to AWS, delivering significant cost reductions and operational efficiencies for multiple organizations. Her expertise spans designing and implementing scalable, highly available solutions that leverage advanced AWS services to solve complex business challenges. She excels at integrating AI/ML capabilities within cloud infrastructure to enable intelligent, data-driven decision-making and automation. Known for her leadership in technological transformations, she has successfully delivered cutting-edge solutions using containerization, serverless technologies, and modern architectural principles, helping organizations streamline operations and achieve measurable business outcomes while maintaining high standards of reliability and security.