For decades, we have built deterministic software systems - our architectures, tools, and mental models are optimized for this world. AI software development breaks that mindset. In AI products, software architecture is only the tip of the iceberg. Beneath it lies an evolving data architecture: prompts, retrieval strategies, eval loops, and knowledge graphs that directly shape system behavior over time. Shipping code is only the start of that lifecycle. In this talk, I’ll share how we approach this shift at Typeform as we develop a framework for AI development grounded in agentic RAG. I’ll cover how we evolve both our system architecture and engineering practices—from orchestrating agents using A2A/ MCP patterns, to revamping developer workflows with continuous evals, observability, and tooling that allows teams to safely evolve prompts, graphs, and retrieval strategies. A core focus will be how we design an evolving user knowledge graph, and how RAG acts as shared infra powering all of our AI features. This session is for engineers navigating the transition from deterministic software to AI-native systems, looking for practical patterns to build and operate RAG in production.
Susanna Wong is a Staff Engineer at V7 Labs and a Google Developer Expert with a focus on JavaScript, web technologies, and machine learning. She is a member of the Google Developer Advisory Board and is known for bridging front end engineering with practical ML applications through speaking, teaching, and community work.
