Chalk at Fintech Devcon 2026

Model development is often slowed down by long iteration cycles, too many tools, and frequent handoffs. We learned this the hard way working with an enterprise building real-time fraud detection models for card authorization.

This talk tells the story of building an AI agent that helps data teams investigate missed fraud, analyze model behavior, and automatically propose new rules and features. We realized early on that the agent needed access to the same context as the model, but the infrastructure underneath couldn't support that.

The core of the session walks through rebuilding the foundation around a real-time context layer that computes fresh data from the source at inference time. We’ll highlight the limitations of the original batch-first systems: stale features, train/serve skew, inconsistent feature definitions, and latency constraints. Then, we'll discuss key agent and model engineering decisions, including navigating deployment model constraints, managing tradeoffs between freshness and speed, and treating observability as a requirement.

Attendees will leave with a clear understanding of where batch-first stacks break under real-time demands, how the shared context layer improves models, and how AI agents can turn model development into a continuous cycle.