Teaching the Streamhouse to Speak Human with LLMs and MCP

Imagine asking "What changed in the last few minutes, and who or what is affected?" and getting an answer based on live data, with events connected, context built in real time, and structured sources you can trust.

This is where the Real-time Context Engine meets the Streamhouse: turning constantly changing data into context that LLM-powered agents can explore, query, and reason over. But it takes more than just plugging an LLM into a data source.

We'll follow events from where they're created all the way into the Streamhouse, where schemas help us make sense of the data and manage changes. We'll explore when to append events to keep the full history and when to upsert records to maintain the latest state. Then we'll see how stream processing turns all of this into queryable tables, powered by a low-latency query engine.

Next, we'll see what happens when a user asks a question. An LLM-powered agent can discover available data, explore schemas and metadata, and run bounded queries using tools like Model Context Protocol. We'll break down how it all fits together—and what it takes to make it work reliably in production: handling late events, access controls, query limits, failures, freshness, and making sure people can verify the answers.

By the end, we'll have connected the dots from streaming events to answers people can actually use, with the context and controls to trust them.