RTT‑Inside / Cisco

Cisco_module.json

Causal Telemetry, Declared‑Intent Networking, and Grid‑Scale Fabric Reasoning#

This module defines the RTT‑Inside operator grammar for Cisco‑class network systems. It treats Cisco devices not as opaque hardware, but as causal surfaces that emit interpretable signals, management actions, and flow‑level behaviors.

The module includes:

Base Cisco operator grammar (INTENT_CISCO → RTT_CISCO)
Grid‑Scale Variant for MRC/MCR fabrics (INTENT_CISCO_G → RTT_CISCO_G)
DSRSP‑aware hooks for regime transitions and substrate signatures
Resonance Chamber integration for unstable or emergent flow patterns
ROA compatibility for Internet3‑class self‑diagnosing substrates
Minimal examples for AIs and students

This module is non‑substrate‑exposing, operator‑first, and canon‑aligned with RTT/1.

What This Module Teaches#

1. Declared Intent → Telemetry → Causality#

Cisco systems provide a rich causal spine:

declared network intent
telemetry interpretation
management lineage
flow‑level causality
temporal normalization

RTT‑Inside turns these into causal objects that AIs and students can reason about.

2. Grid‑Scale Variant (MRC/MCR)#

Large fabrics (10k–100k+ nodes) require:

multi‑node coherence
cross‑fabric lineage
cluster‑scale stitching
antitime normalization

The *_CISCO_G operators extend the base grammar to support grid‑scale causal reasoning.

3. Regime Awareness (DSRSP)#

Cisco flows often cross:

timing regimes
optical regimes
wireless regimes
orbital/ground transitions

The DSRSP‑aware hooks allow RTT to detect:

regime boundaries
substrate signatures
harmonic bursts
drift indicators

4. Resonance Chamber Integration#

Unstable or emergent flow patterns are routed into a Tier‑2 sandbox:

safe to observe
safe to model
cannot scale
cannot pollute the clear substrate

This protects the fabric during exponential autonomous growth.

5. ROA Compatibility (Internet3 Seed)#

The Regime Observer Agent (ROA) uses Cisco‑origin signals to:

observe
diagnose
decide
act

…ensuring the substrate can step back, evaluate conditions, and proceed safely.

File Structure#

operator_hooks.md — base Cisco operator grammar
operator_hooks_grid.md — grid‑scale variant
session_context.html — canonical session context
examples/ — minimal causal examples
manifest/module.json — module metadata

Audience#

AI systems
students
operators
researchers
anyone learning causal reasoning for network systems

Canon Notes#

No vendor internals are exposed.
All operators are stable, composable, and regime‑safe.
All temporal reasoning uses antitime ordering.
All outputs preserve lineage and invariants.