AI applications are rapidly evolving from simple prompt-response systems to autonomous agents capable of reasoning, using tools, and executing complex workflows. However, while many developers can build impressive demos, very few systems are designed to operate reliably in production.
This talk explores how to design production-ready AI agent systems using modern cloud architecture patterns inspired by Google Cloud infrastructure. We will walk through the architecture behind modern agent systems and examine how orchestration layers, tool integrations, memory management, and observability work together to enable reliable automation.
Using real engineering patterns, the session will demonstrate how components such as Vertex AI-style model inference, serverless execution layers similar to Cloud Run, event-driven messaging, and persistent memory stores can be combined to build scalable AI agents capable of interacting with APIs, processing tasks, and executing workflows autonomously.
Attendees will gain practical insights into designing AI systems that move beyond prototypes and deliver real operational value. The session will include a live demonstration of an AI agent system running locally while reflecting the architecture used in production cloud environments.
Whether you are building internal automation tools, developer assistants, or intelligent workflow systems, this session provides architectural guidance for taking AI agents from experimentation to production deployment.