Inference Engineering treats the moment of AI execution as a governed, managed event.
Rather than just making models faster, this pillar focuses on making inference observable, policy-bound, and structurally sound through context brokerage, dynamic routing, and semantic cache management.
Overview
How do we treat context assembly and dynamic prompting as a platform service rather than application logic?
How can semantic caching act as an authorization boundary, not just a latency optimization?
How do we route requests across local and cloud models based on cost, policy, and capability?
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Recent research
[Inference Ops]
When an agent burns its budget and dies at the turn cap, the instinct is to raise the cap. But roughly 99% of an attempt's tokens are context re-carried through every turn, so a defect that strikes early taxes every turn after it. One audited run spent about 40% of its turns re-deriving a defect already on file — and the fix was one line.
[Inference Ops]
Semantic caching reuses model responses by meaning rather than exact match. That turns the cache into a place where authorization decisions are made, not just a latency optimization.
Artifacts
Inference
A local-first reference architecture for Enterprise DecisionOps, demonstrating how AI agents operate safely by mapping runtime setup to verifiable audit evidence.
