Inferoa: Inference-native Tokenmaxxing Agent Harness for Loop Engineering
Most agents call models as if inference were a black box.
The agent loop lives in one place, routing policy in another, serving behavior somewhere else, and context management becomes a last-minute fight with the window. That split is tolerable for one-turn chat. It breaks down when agents run for hours, recover from failures, compress context, warm prefix cache, route between model paths, and still need to prove the work at the end.
Prefix cache stability is ignored. Routing is bolted on later. Context is pasted until it fits. Users pay for that gap.
Inferoa = Infer(Inference-native)o(Tokenmaxxing Loop Engineering)a(Agent Harness).
Inferoa is an Inference-native Tokenmaxxing Agent Harness for Loop Engineering. It is built for recursive long-horizon goals: define the outcome once, then the agent loop keeps inspecting, changing, testing, reflecting, and continuing until the work is proven.
That is what inference-native means here: Inferoa starts from the inference stack and co-designs loop engineering around tokenmaxxing: prefix-cache discipline, context optimization with RTK and CodeGraph, intelligent routing through vLLM Semantic Router, high-throughput vLLM serving with vLLM Engine, vLLM Omni multimodal capability, and native goal, plan, and autoresearch loops with tokenmaxxing observability.
