We are entering the Era of Experience, where AI agents will transform customer journeys by learning directly from interactions. But most customer-facing agents today are “senseless,” lacking the real-time context needed to deliver relevant, empathetic, and valuable experiences. This session will explore how real-time streaming architectures and proprietary customer data can power the next generation of intelligent, perceptive agents.

Join Snowplow’s Jon Su as he unpacks:

• Why brands risk commoditization if they rely on third-party agents
• How real-time context enables smarter, more personalized customer interactions
• The key ingredients for building agents that perceive, adapt, and self-optimize
• How Snowplow Signals provides the real-time customer intelligence foundation for agentic applications

Discover how to shift from static personalization to adaptive, agent-driven experiences that improve customer satisfaction, loyalty, and business outcomes.

AI-powered development tools are accelerating development speed across the board and analytics event implementation is no exception to this, but without appropriate usage they’re very capable of creating organizational chaos. Same company, same prompt, completely different schemas—data teams can’t analyze what should be identical events across platforms.

The infrastructure assumptions that worked when developers shipped tracking changes in sprint cycles or quarters are breaking when they ship them multiple times per day. Schema inconsistency, cost surprises from experimental traffic, and trust erosion in AI-generated code are becoming the new normal.

Josh will demonstrate how Snowplow’s MCP (Model Context Protocol) server and data-structure toolchains enable teams to harness AI development speed while maintaining data quality and architectural consistency. Using Snowplow’s production approach of AI-powered design paired with deterministic implementation, teams get rapid iteration without the hallucination bugs that plague direct AI code generation.

Key Takeaways:

• How AI development acceleration is fragmenting analytics schemas within organizations

• Architectural patterns that separate AI creativity from production reliability

• Real-world implementation using MCP, Data Products, and deterministic code generation