Problems / Multi-agent and harness, hard
Every subagent sends 3,000 tokens of context. Eight subagents per question multiply token use past the provider quota.
Free to open. Runs in your browser, no sign-up.
Success ≥ 95% of tasks, p99 ≤ 90 s.
2 requests a second for 45 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 Subagent pool.An agent loops: think, call a tool, read the result, repeat until done. | 400 slots · 1 steps |
| Subagent poolSubagent pool | Agent orchestrator calls it.Calls Model.Multi-agent research systems fan a task out to parallel subagents and use many times more tokens than a single agent. | 400 slots · 8 steps |
| ModelLLM endpoint | Subagent pool calls it.Latency has two parts: time to the first token, then a steady stream of tokens per second. | 300 streams · 2M input tokens/min |
Try it first. Open one hint at a time if you get stuck.
Multi-agent systems use many times the tokens of one agent.
Shared instructions repeat in every subagent.
Prefix caching (and fewer subagents for simple questions).