AI Agents are Only as Smart as their Context: Building a Real-Time Context Engine

The industry has poured billions into LLMs and AI agents, yet many deployments stall in pilot because they lack one thing: fresh context. If your AI agent is reasoning over stale batch data or fragmented profiles, it cannot deliver real enterprise value. The missing piece isn’t a bigger model, it’s a real-time context engine: a streaming feature store that supplies an up-to-the-second 360° view of the customer, and that actively notifies agents when something they care about changes. This session walks through how we built exactly that at Intuit. The Customer Data Cloud platform ingests data from hundreds of sources, processes data through config-driven Apache Flink pipelines, maintains unified profiles in a low-latency NoSQL store, and serves them via a GraphQL Read API. But the story doesn’t end with the read path, every profile update publishes a real-time change notification on Kafka, which forms the backbone of an event-driven AI loop: agents and automation systems subscribe, react to changes the moment they happen, and drive in-product personalization and marketing workflows in real time. We’ll cover three ideas developers can take home:

• Context over Models: Why a streaming 360° view of the customer matters more than the specific LLM you pick — and how to architect for it.
• The Event-Driven AI Loop: Using Kafka change notifications as triggers for autonomous agentic workflows.
• Privacy-by-Design in the Stream. Enforcing user consent and asset-based access inside the streaming fabric itself, so compliance is a property of the data, not a layer bolted on top.

Expect architecture diagrams, concrete Flink-pipeline patterns, a live walkthrough of a change notification firing an agent, and the honest lessons from operating this in production.