Snowflake and Databricks held their flagship summits within weeks of each other this year, and something notable happened: two platforms that started from opposite ends of the data world — one from the data warehouse, one from the data lake — announced product strategies that look nearly identical. Both are betting heavily on AI agents as the primary unit of compute, both are adopting the Model Context Protocol as a connective layer, and both are extending governance controls to cover AI systems directly, not just human users.
When competitors with different histories independently arrive at the same roadmap, it usually means the market has already made the decision for them.
What “Agents as Compute” Actually Means for Cost and Complexity
This convergence isn’t just a technology story — it’s a cost and governance story. Agentic workloads behave very differently from traditional BI queries. They run iterative, exploratory queries rather than scheduled reports. They don’t have a shared, stable definition of what counts as an authoritative table or a trusted metric across an organization. And they can generate query volume that looks nothing like historical usage patterns, which is exactly the kind of unpredictable load that consumption-based pricing models are built to monetize.
Snowflake’s own summit messaging leaned into this directly, with leadership describing a future where employees are guiding intelligent agents that operate across the business continuously and autonomously. That’s a genuinely important shift in how enterprise data gets used. It’s also a shift that, on a metered platform, can turn a well-understood analytics budget into a much harder number to forecast.
Governance Is Catching Up, But Slowly
Both platforms are adding controls aimed at agent-generated activity — monitoring for data exfiltration, prompt injection, and unauthorized access as AI systems increasingly touch the same data warehouses that power core business reporting. That’s a necessary evolution. But it also means data teams are being asked to govern a new category of user — autonomous agents — on top of infrastructure that was, until very recently, designed primarily for human analysts and scheduled jobs.
The organizations best positioned for this shift are the ones with a clear, governed compile path from raw data to whatever’s consuming it next, whether that’s a dashboard or an autonomous agent. Ungoverned, ad hoc access by agents into production data is where the real risk sits.
Performance and Predictability Still Matter Most
Agentic workloads amplify a problem that’s existed all along: unpredictable, high-concurrency demand needs a warehouse that can absorb it without buckling on performance or exploding on cost. Yellowbrick’s architecture was built for exactly this kind of concurrent, high-throughput SQL workload, with predictable infrastructure economics that don’t move in lockstep with every new agent your organization deploys. As agentic AI systems increasingly query live data at machine speed rather than human speed, the underlying warehouse needs to deliver consistent, low-latency performance regardless of who — or what — is asking the question.
The Takeaway
When your two biggest platform vendors independently land on the same strategic bet, it’s a strong signal the shift is real. But convergence on strategy doesn’t mean convergence on cost or governance outcomes. As agents become a bigger share of who’s querying your data, the infrastructure underneath needs to be ready for workloads that are constant, concurrent, and far less predictable than the reporting cycles most data warehouses were originally built around.
