Summary: Specs and whitepapers tell you *what* SI-Core is. This article answers a different question:
> “If I’m on call for an SI-Core / SI-NOS stack wrapped around LLMs and tools, > *what do I actually look at — and what do I do when it goes weird?*”
It’s an operator’s guide to running Structured Intelligence in production: how CAS, EAI, RBL, RIR, SCover, ACR, etc. show up on dashboards, how to set thresholds, and how to turn incidents into structural learning instead of panic.
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Why It Matters:
* Bridges *theory → SRE/MLOps practice* for SI-Core & guardrailed LLM systems * Shows how to treat metrics as *symptoms of structural health*, not vanity numbers * Gives concrete patterns for *alerts, safe-mode, rollback tiers, and ethics outages* * Helps teams run SI-wrapped AI systems *safely, explainably, and audibly* in real environments
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What’s Inside:
* A day-to-day mental model: watching *structure around the model*, not just the model * Ops-flavoured explanations of *CAS, SCI, SCover, EAI, RBL, RIR, ACR, AES, EOH* * Example *“SI-Core Health” dashboard* and green/yellow/red regions * Alert tiers and playbooks for: ethics degradation, rollback integrity issues, coverage gaps * A walkthrough of a realistic *ethics incident* from alert → investigation → rollback → lessons
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📖 Structured Intelligence Engineering Series
This piece sits next to the SI spec and Evaluation Pack as the *runbook layer* — for SRE, MLOps, and product teams who actually have to keep structured intelligence alive in prod.