How to know a self-hosted model is ready for an agent
A may look good on normal benchmark tests but still fail during long agent runs. The hard question is whether it can handle many tool calls in a row without losing quality, timing out, or breaking under real traffic. Readiness depends not only on the model, but also on serving choices such as runtime, , and KV cache settings.
Load matters because many requests arriving at once can change failure rates and response quality. Teams also need to know whether they have a real pre-deployment check or whether they mostly launch first and watch for problems afterward. Ownership matters too: the decision may sit with ML, platform, , or no clear team at all.
The practical need is a way to test agent readiness before production, especially for tooling around that problem.
Key points
- Standard may not predict whether an agent survives long multi-step work.
- Runtime, , and KV cache settings can change real-world results.
- Heavy traffic can increase timeouts, failures, and quality drops.
- Teams need a clear readiness check before production, not only monitoring after launch.
- Someone should own the go-live decision across ML, platform, or .