In the span of a few weeks, three of the largest names in data infrastructure made announcements that, read together, say something important about where enterprise AI is heading.
On September 24, Google announced PostgreSQL for agents in AlloyDB, now in preview. It provisions sandboxed database instances in seconds, giving agents read-only access to production data that is up to the second. Agent workloads stay isolated from primary, standby, and read-replica production systems. The instances scale to zero when the agents finish, and Google says the architecture is designed to handle bursts from a few agents to millions.
Two days earlier, Teradata recast its Tera assistant as an “agentic coworker.” It introduced the Tera Context Engine and Tera Harness, which embed policy controls and human approvals directly in the agent loop and block high-risk actions before they execute. Teradata also put efficiency front and center. In its own benchmark testing against Claude Code on the same underlying model, it reported 73% fewer tokens consumed and 58% lower total cost.
And in July, Snowflake introduced Cortex AI Gateway to centrally govern first- and third-party agents, including tools like Claude Code and Cursor. The gateway records agent activity and adds AI-consumption visibility, cost attribution, and spending limits.
Each of these is a different product, solving a different slice of the problem. But they share one premise: AI agents don’t behave like the users data platforms were built for, and they need their own controls.
Why agents break the old workload model
For decades, data platforms were designed around a familiar mix: scheduled ETL, dashboard refreshes, and a known population of analysts running ad-hoc queries. Capacity planning, concurrency limits, and cost models all assumed that mix.
Agents don’t fit it. A single agent working through a task can fire off dozens of queries as it reasons, retrieves, checks its work, and tries again. Multiply that by hundreds of agents running on schedules no one explicitly set, and you get bursty, unpredictable demand that lands on the same infrastructure serving the CFO’s dashboard and the overnight finance close.
The industry’s response so far tells you where the risks sit:
- Isolation. Google’s approach keeps agents off production instances entirely, so a runaway reasoning loop can’t degrade business-critical workloads.
- Budgets. Snowflake’s spending limits and Teradata’s token-efficiency claims both acknowledge that agent consumption has to be bounded and measured, not just enabled.
- Guardrails. Teradata’s human approvals and Snowflake’s task-scoped access recognize that agents need narrower permissions than the people who deploy them.
These are the right instincts. The open question for data leaders is whether those controls live in a separate layer bolted onto the stack or are built into the data platform where the queries actually run.
Governance belongs where the work happens
Gateways and orchestration layers are valuable. But every agent request eventually becomes a query against real data, competing for real compute alongside every other workload in the enterprise. If the data platform underneath can’t separate, prioritize, and cap that work, governance upstream only goes so far.
That’s why workload management, a capability that rarely makes headlines, is about to matter a lot more. The questions worth asking any platform are straightforward:
- Can agent workloads run on compute that is separate from business-critical reporting?
- Can you set hard resource limits so one workload can’t starve another?
- Can you spin agent capacity up for a burst and drop it when the work is done?
- Does the data stay inside your own security boundary while all of this happens?
Where Yellowbrick fits
Yellowbrick was designed around exactly these questions, long before “agentic” entered the vocabulary. Its rules-based workload management assigns resources to each query, with hard resource controls that keep queries from starving one another. As a result, long-running ad-hoc queries don’t obstruct business-critical ones. Elastic virtual compute clusters can be created, resized, and dropped on demand through SQL. That makes it practical to give agent workloads their own dedicated compute, separate from the clusters serving finance or operations, and to retire that capacity when it’s no longer needed.
Yellowbrick also runs in the customer’s own cloud account or on-premises, so agents query data that never leaves the enterprise’s network for an external SaaS provider.
The vendors announcing agent infrastructure this fall are right that agents are a new class of workload. The enterprises that scale them safely will be the ones whose data platforms already know how to isolate, prioritize, and bound demand, instead of discovering the need after an agent’s first unplanned weekend of queries.
Related Resources
- Enterprise Data Warehouse
- 83% of Organizations Aren’t Ready for Production-Grade Agentic AI. Here’s What Their Data Platforms Are Missing.
- Snowflake and Databricks Just Converged on Agents. Here’s What That Means for Your Data Strategy.
- Why AI copilots need a modern, high-concurrency SQL platform behind the scenes
- What Is Workload Management and Why Do I Work on It?