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Single-path output. Integrates the TAO Optimization Inversion framework (TAO Paper Revision — OSDG Experiment Draft (v2.2)), Yennefer thermodynamic routing (‣), architect-tier invariants (Greg × /maru — Architect Review), and the UCP Decision Matrix (Genesis Conductor × UCP — Integration Spec & Model Attainment Decision Matrix) into a unified agent-based intent router backed by Dissonance Memory MCP for cross-session state and TAO-derived energy-aware scheduling.

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Canonical cluster anchors (previewable). Yennefer cluster · TAEX · Retraining loop · §6.17 · Integrations registry · Integrative Flow.

‣ · ‣ · ‣ · ‣ · ‣ · ‣ · ‣ · ‣

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A2A Operationalization · Surface 2.2 (Intent router). Canonical index: A2A Operationalization Index — Spine Footers, JSONL Prompts, Model Variants, Permissions.

{"v":"a2a/1.0","kind":"intent.route","id":"taex.route.001","surface":"<https://www.notion.so/c33eb0932f0844a8a2a9cd631e180e30","input":{"intent_text":"><raw_intent>","workspace_id":"<ws>","policy_tier":"T2"},"pipeline":["classify","memory.recall","memory.dissonance_check","decision_matrix","yennefer.eta_thermo","aal.submitTask","memory.store"],"model":{"classifier":"claude-3-5-sonnet","guardian":"claude-opus-4-1","reframe":"claude-3-5-sonnet"},"exec":"cloudflare_worker:gc-mcp-server","capsule_event":"taex_routing.routed"}

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Layer 1 — Solution

1. What Is TAEX

TAEX = TAO + Attestor + EXtensions. It is the composition of:

TAEX is the runtime identity of the Genesis Conductor mesh when operating in intent-routed mode.

2. What Is Dissonance Memory MCP

Dissonance Memory is a Model Context Protocol (MCP) server that provides cross-session, cross-agent episodic and semantic memory with built-in cognitive dissonance detection. When an agent's current intent conflicts with prior commitments, beliefs, or workspace state stored in memory, the Dissonance layer surfaces the conflict as a structured signal before routing proceeds.

MCP surface: mcp.genesisconductor.io/memory/dissonance

Core operations:

MCP Tool Description Returns
memory.recall Retrieve episodic + semantic memories relevant to the current intent { memories[], relevance_scores[], dissonance_flags[] }
memory.store Persist a new memory entry (event, decision, commitment, belief) { memory_id, stored_at }
memory.dissonance_check Compare a proposed action against stored commitments and beliefs { dissonance_score, conflicts[], resolution_suggestions[] }
memory.project Build a state projection from memory for a given workspace/scope { projection, confidence, staleness_ms }
memory.forget Soft-delete a memory (mark as superseded, retain audit trail) { forgotten_id, reason }

Dissonance scoring:

$D(a, M) = \max_{m \in M} \text{conflict}(a, m) \times \text{recency}(m) \times \text{confidence}(m)$

where $a$ is the proposed action, $M$ is the memory set, and $D > 0.3$ triggers a Guardian review before routing proceeds.

3. Intent Router Architecture