Agent-Aware Infra: Optimizing Inference via Cache and Scheduling
_ScottCondron · x · 2026-08-23
AI harnesses are evolving from squeezing model performance to optimizing inference infrastructure for efficiency. The trend is toward "agent-aware" infrastructure that understands agent and session contexts to optimize KV-cache reuse, retention/prefetch across tool calls, and priority scheduling. Projects like Dynamo's nvext.agenthints and llm-d's program-aware serving are pioneering cache-aware routing, speculative prefill, and lifecycle-aware cache management. A cited perspective adds that model capability is fundamental: AI product work involves clawing back capability via in-context learning and turning frontier failures into post-training data to shift capabilities left into the model, ultimately making the harness obsolete.
Related event: NVIDIA details agent-aware inference caching with up to 97% hit rate(2 posts)→
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