Focusing on 'Cost per Outcome' rather than 'Cost per Token' is a vital shift for AI reliability. At SDAG [https://github.com/alexbuiko-sketch/SDAG-Standard], we’ve been looking at the same problem from the opposite end of the stack: the hardware-inference interface.
In a distributed system using OpenTelemetry, a 'successful outcome' often hides a lot of silent technical debt. If an event requires 4 retries, it’s not just a billing issue—it’s a signal of high routing entropy. We’ve found that failed attempts or long CoT (Chain of Thought) loops often correlate with specific hardware stress patterns and memory controller 'redlining.'
Integrating SDAG signals into something like your event_id tracking could be powerful. It would allow teams to see not just how much a success cost, but whether the 'path to success' was physically efficient or if it was stressing the cluster due to poor routing logic. Have you considered adding hardware-level telemetry (like jitter or entropy metrics) to your outcome tracking to predict which 'runs' are likely to fail before they even finish?"
Exactly. What you describe as 'parsing work' is, at the architectural level, a high-entropy search across the attention heads. When a prompt is a 'wall of text,' the model's routing logic has to maintain multiple competing states, which physically manifests as jitter and increased power draw per token.
By using semantic blocks (like in your flompt framework), you are essentially performing Inference Pre-conditioning. You’re forcing the model into a narrow, low-entropy path from the very first token.
This is why we focus on SDAG [https://github.com/alexbuiko-sketch/SDAG-Standard] — to provide a metric for this 'routing efficiency.' In the future, we might even be able to use SDAG signals to 'score' prompt architectures like flompt based on how much hardware-level stress they reduce. Structural clarity isn't just a convenience for the model; it's a physical optimization of the compute cycle.
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.
This is a brilliant breakdown of the 'Token Mix' paradox. It aligns perfectly with what we’ve been seeing while developing SDAG.
When you optimize for a structured context payload (like your dependency graph), you aren't just hitting the Anthropic pricing cache—you are literally reducing the routing entropy at the inference level. High-noise inputs force the model into 'exploratory' output paths, which isn't just expensive in dollars, but also in hardware stress.
We found that 'verbose orientation narration' (the thinking-out-loud part) correlates with higher entropy spikes in memory access. By tightening the input signal-to-noise ratio, you're essentially stabilizing the model's internal routing. Have you noticed any changes in latency variance (jitter) between the pre-indexed and ad-hoc runs? In our tests, lower entropy usually leads to much more predictable TTFT (Time To First Token).
A decade since AlphaGo, and we’re still just scratching the surface of model alignment and efficiency. While DeepMind proved that AI can master intuition, we are now looking at the other side: the physical cost of that intuition.
I’m currently preparing a 'match' against KataGo, but not by out-calculating it in the traditional sense. We are using SDAG [https://github.com/alexbuiko-sketch/SDAG-Standard] to monitor routing entropy and hardware stress during high-load inference. The goal is to see if we can identify 'hallucination paths' through hardware-level signals before they manifest in the game logic. It feels like the next frontier of auditing: treating LLMs/RL agents not just as math, but as physical systems with detectable stress patterns.