Micro-Agent: Beat Frontier Models with Collaboration Inside Model API
Which summary reads better? Pick one — models revealed after.Both summaries are AI-generated.
A single OpenAI-compatible model call can now be backed by a bounded “micro-agent” runtime in vLLM Semantic Router: route, fan out, quorum, verify disagreement, synthesize, and repair outputs behind one stable model name. For production systems, this moves agent orchestration out of app code and into serving, letting teams spend extra inference only on hard or risky requests while preserving the same API surface and budget controls.
A router-level "looper" turns a single OpenAI-compatible model call (model: "vllm-sr/auto") into a bounded multi-agent collaboration inside the serving layer — escalating only hard cases via confidence thresholds, fan-out quorums, and disagreement checks under a hard budget cap, all returned as one normal response. This means you can get frontier-competitive quality by orchestrating cheaper/open models without building your own agent graph or changing client code, shifting the cost/quality tradeoff into the router config rather than your application logic — but it also moves latency, budget, and failure-policy decisions into an opaque serving primitive you'll need to observe and tune carefully.