Problems / Agents and tools, medium
A finance agent asks a human to approve every payment. Five reviewers take about 20 s each; tasks arrive at 0.5 a second.
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Success ≥ 95% of tasks, p99 ≤ 3 minutes.
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 5; 2 of them are hidden until then.
0.5 requests a second for 60 seconds: 100% task (writes).
What each component does in this design, and what calls it. The simulator mimics each one from these settings.
| Component | Its job here | Settings |
|---|---|---|
| Load balancerLoad balancer | Traffic 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 service | Load 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 orchestrator | Agent API calls it.Calls Model, Human approval.An agent loops: think, call a tool, read the result, repeat until done. | 400 slots · 1 steps |
| ModelLLM endpoint | Agent orchestrator calls it.Latency has two parts: time to the first token, then a steady stream of tokens per second. | 300 streams |
| Human approvalHuman approval | Agent orchestrator calls it.Human-in-the-loop review holds risky actions until a person approves them. | 5 slots |