> User → LLM API → ad-hoc glue → DB / APIs / users
Powerful, but missing structure: no clear observation contract, ethics lens, rollback path, or durable audit. This article proposes a *transitional architecture* where today’s LLM stacks are *wrapped by SI-Core / SI-NOS* rather than replaced — turning LLMs into *proposal engines* and SI-Core into the runtime that constrains, simulates, and rolls back their effects.
> Don’t ask the LLM to be “aligned on its own” — > *surround it with a runtime that knows what it’s doing.*
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Why It Matters: • Gives you a *realistic bridge* from current LLM agents to SIL-native systems • Makes prompts, goals, identity, and ethics *first-class objects* • Adds *rollback and effect ledgers* so external actions are reversible and auditable • Works with *existing* models — no retraining required
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What’s Inside: • High-level architecture: SI-Core on top, LLM wrapper as jump engine, tools as effect layer • How to wrap an LLM call as a *JumpRequest / JumpResult* instead of a raw response • Observation → prompt → proposal flows tied to [OBS]/[ID]/[ETH]/[EVAL]/[MEM] • Tool use as *declarative actions*, executed via effect ledgers and compensators • Degradation modes: parse failures, schema violations, policy rejections, LLM outages • A staged *migration path*: from “raw LLM agent” → “wrapped” → *goal-native + SIL* cores
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📖 Structured Intelligence Engineering Series
This piece continues the *SI-Core in Practice* line, showing how to put a structured runtime *around* today’s LLM systems instead of throwing them away.
Summary: “Intelligent”, “aligned”, “resilient” — we use these words a lot, but what do they *numerically* mean? This article turns Structured Intelligence from a philosophy into something you can *monitor, compare, and debug*.
It pulls together metrics like *CAS, SCI, SCover, EAI, RBL, RIR* and friends into a coherent evaluation layer for SI-Core, SIC, and AGI-adjacent systems.
> If you can’t measure how structure behaves under stress, > *you can’t claim it’s intelligent — only hopeful.*
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Why It Matters: • Moves beyond leaderboard benchmarks to *system-level behavior metrics* • Lets you track *causality alignment, rollback safety, ethics gating, and coverage* as first-class signals • Provides a common language for *researchers, infra teams, and policy folks* to talk about “how aligned” a system really is • Bridges day-to-day engineering KPIs with *cosmic-scale metrics* introduced in the broader Structured Intelligence work
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What’s Inside: • A clean overview of core metrics (e.g. CAS, SCI, SCover, EAI, RBL, RIR) and what they *actually* tell you • How to instrument SI-Core / SIC stacks so these numbers fall out of normal operation • Examples: “good” vs “bad” metric profiles for agents, rollbacks, effectful tools, and governance loops • How micro-level metrics roll up into *macro and cosmic indicators* (e.g. structural resilience, long-horizon stability) • Practical notes on logging, sampling, and avoiding KPI theater
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📖 Structured Intelligence Engineering Series
This piece is the metrics counterpart to the architectural articles — turning Structured Intelligence from “just very coherent” into something you can *graph, alert on, and iterate*.
