Your agents will survive scale. The harder question is whether you can afford it. When a model writes your access patterns at runtime, every new query shape tends to buy you another system, another pipeline, and another bill.
Worse, four async copies of the same data can’t guarantee they reflect the same moment, at any budget. And once agents hit production, traditional telemetry can’t show the full workflow behind their decisions.
In this technical deep dive, AWS, TiDB, and Grafana Labs show how to run agents on one engine for every query shape and observe them end to end, including a live demo.
What you’ll learn:
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- Why agent workloads break design-time indexing when a model composes your queries at runtime.
- Why consistency, not latency, is the real cost of running four separate databases.
- How to run point lookup, semantic, full-text, and aggregate queries over the same rows in one SQL statement.
- How Manus runs about 1M agent tenants and a 70B-row table on one engine, with no sharding.
- How Plaid’s six engineers migrated 234 databases off Aurora MySQL without rewriting their applications.
- How to observe agent workflows end to end, from tool calls to infrastructure, in a live Grafana Cloud demo.
