[nicola_alessi]

Interesting framing — hadn't thought about it from the inference routing angle but it maps well to what the data shows. On latency variance: yes, significantly. Cost standard deviation across runs dropped 6-24x depending on task type. The most extreme case was a refactoring task: baseline sigma $0.312 vs $0.013 with pre-indexed context. Duration variance also dropped in 6 out of 7 tasks. I didn't measure TTFT specifically but the overall duration went from 170s → 132s with much tighter clustering around the mean. The stabilization effect is probably the most underrated finding. Everyone focuses on the average cost reduction, but the predictability improvement matters more for production workloads — you can actually forecast spend instead of hoping the agent doesn't go on an exploration tangent. What's SDAG? Curious about your setup.

[alexbuiko]

Those sigma numbers are incredible—dropping variance by 24x practically confirms that you’ve managed to 'trap' the model in a low-entropy state. In production, predictability (the 'anti-tangent' factor) is often worth more than the raw discount.

SDAG (Systematic Defect Awareness & Guidance) is a protocol we’re developing for auditing AI infrastructure at the hardware-inference interface.

Most observability tools look at the 'what' (tokens, logs), but we look at the 'how' (routing entropy and hardware stress). We use it to detect when a model's routing logic starts 'redlining' the hardware—essentially catching those exploration tangents you mentioned by monitoring physical signals like memory controller stress and cache thrashing before they even manifest as high latency or cost spikes.

We're currently open-sourcing the core SDK [https://github.com/alexbuiko-sketch/SDAG-Standard]. Given your results, I’d be very curious to see if your 'pre-indexed context' approach shows a direct drop in hardware-level jitter. It sounds like you've found a software-level 'clamp' for what we’ve been measuring as physical entropy.