One post tagged with "inference"

The most interesting agent work is moving from better prompts to better loops.

Loop Engineering means giving the model a goal, feedback, verification, memory, and tools, then letting it self-correct until the work is proven. Primitives like /goal, rubric-driven outcomes, verifier sub-agents, and memory-backed sessions matter because they move the work from "prompt the next answer" to "design the system that keeps improving."

That loop is also an inference workload. As turns accumulate, prompt prefixes drift, cache reuse collapses, stale evidence fills context, model routing gets harder, and serving choices start to matter.

That is where Loop Engineering has to become inference-native. A long-horizon loop needs to see the substrate it is consuming: tokens, cache, context, routes, endpoints, and model capacity. Tokenmaxxing is the discipline of keeping those surfaces explicit so every horizon can reuse, compress, route, and recover instead of sending another blind chat turn.

That is the gap Inferoa is built around. The name is deliberately literal:

Inferoa = Infer(Inference-native)o(Tokenmaxxing Loop Engineering)a(Agent Harness).

Inferoa is an Inference-native Tokenmaxxing Agent Harness for Loop Engineering. It brings the pieces a serious loop needs into one runtime: goal/rubric feedback, verification evidence, memory and context control, prefix-cache discipline, intelligent routing through vLLM Semantic Router, high-throughput serving with vLLM Engine, vLLM Omni multimodal capability, and tokenmaxxing observability across every turn.