We spent last week at Snowflake Summit. The question we heard more than any other: how do we reliably trust agents in production?
Four years ago, we bet that real-time data infrastructure would become as essential to AI as databases are to applications. That bet started with traditional ML. Now it runs through the question every enterprise AI team is wrestling with — getting dynamic agents into production safely.
Chalk Compute is our answer: an enterprise runtime for AI agents, model inference, and other workloads. It runs each workload in a secure sandbox inside your own cloud environment, so your code and data never leave your VPC.
Because Compute is built on Chalk's Context Engine, it does what general-purpose runtimes can't. It serves your agent historical production data, making production-grade agent evaluation possible before anything ships.
When an agent runs, it makes a series of calls and queries. Every one of those steps is anchored to the same moment in time. The agent can't accidentally read data that doesn't yet exist. That's what makes real backtesting possible.
What We Saw on the Show Floor
Two things from the show floor are worth sharing.
AJ Balance, Grindr's CPO, joined me on stage to walk through how they built an AI-first consumer company at scale: 15 million monthly active users, 65 engineers, and $440 million in revenue. His throughline was direct: the infrastructure question is the product question. Grindr became a globally recognized app by pairing the right features with the right architecture. AJ said it directly:
Chalk Compute was the only integrated compute and context engine for agents that ran entirely inside our own environment, at the scale we needed, without becoming an infrastructure project. What would have taken months took weeks.
Separately, my co-founder Elliot Marx demoed Chalk Compute to a standing-room-only crowd, walking through what it looks like to lock an agent to a point in time and run it against real historical data.
For engineers who have spent years stitching together synthetic test environments and shipping with their fingers crossed, Elliot captured their attention.
Bridging The Temporal Gap
Teams we work with are blocked on agent evaluation. Before any autonomous system touches a production workflow, one question has to be answered: how would this agent have behaved against the data it would actually have seen?
For most teams, the honest answer is that they do not know. They build synthetic test environments, make educated guesses, and ship, hoping it works. When something goes wrong, reconstructing what the agent saw and why it decided what it did is either painful or impossible.
Chalk Compute solves this by letting you lock an agent to any point in time and run it against the data your production system would have actually served at that moment. Not a synthetic approximation. The real thing, inside your own cloud.
The clearest version of this problem is from Grindr. Their requirements were absolute: full data residency, no third-party routing of user data, and the ability to ship agents without triggering a manual security review every time something changed. That meant they needed to test agents against point-in-time production data.
Chalk Compute runs entirely inside Grindr's VPC. Their trust-and-safety systems use agents to run in real time at scale. That’s just one reason they’ve become a leader in consumer AI.
The teams that will win with agents are the ones who treat the data layer as load-bearing from the start. They learned this lesson with ML models. If you're building agents you need to be able to trust and evaluate in production, we'd love to talk.
