Problems / AI products, medium

Data analyst agent and the warehouse

A data-analyst agent writes SQL against a warehouse that runs 8 queries at once, about 800 ms each.

Solve it in the labTake it as a mock interview

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Your goals

Success ≥ 95% of tasks, p99 ≤ 90 s.

Test cases

Like a coding problem, a solution has to pass every case, not just the one it was built for. Submit in the lab runs all 6; 2 of them are hidden until then.

  1. As given. The problem’s own traffic, scheduled failures and seed.
  2. Peak hour. 25% more traffic than the problem states, with the same failures.
  3. A slow dependency. The main data store or model answers twice as slowly for part of the run.
  4. A server dies. One replica of the service with the most replicas is killed for part of the run.

The traffic

4 requests a second for 45 seconds: 100% task (writes).

The system

What each component does in this design, and what calls it. The simulator mimics each one from these settings.

ComponentIts job hereSettings
Load balancerLoad balancerTraffic enters here.Calls Agent API.Layer 4 balancers route by IP and port; layer 7 balancers read HTTP and can route by path or header.–
Agent APIApp serviceLoad balancer forwards to it.Calls Agent orchestrator.Little's law: throughput ≈ concurrency ÷ latency. 200 slots at 50 ms serve about 4,000 req/s.3 replicas × 500 slots · timeout 180 s
Agent orchestratorAgent orchestratorAgent API calls it.Calls Model, Warehouse.An agent loops: think, call a tool, read the result, repeat until done.400 slots · 4 steps
ModelLLM endpointAgent orchestrator calls it.Latency has two parts: time to the first token, then a steady stream of tokens per second.300 streams
WarehouseWarehouse / lakehouseAgent orchestrator calls it.Warehouses store data by column, so analytical queries read only the columns they need.8 slots

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