CME Agent Lab — Multi‑Regime Substrate Agents

RTT / CMH / MSRM — Crystal–Mycelial Engine Teaching Module#

1. Lab Overview#

This lab teaches students how to implement CME‑aware agents that operate across:

• Biological Growth Regime (BGR)
• Hybrid Resonance Regime (HRR)
• Mineral Lock‑In Regime (MLR)

Agents use RTT operator families (P, E, G, M, S, HybridOps) to simulate substrate transitions:

Biological → Hybrid → Mineral

2. Exercise 1 — Biological Trace Agent#

Goal: Capture biological geometry and routing.#

Requirements

• call P.trace_extend
• call G.nutrient_gradient
• store results in bio_map

Starter Scaffold

class BiologicalTraceAgent:
    def run(self):
        # TODO: trace geometry
        # TODO: compute gradients
        # TODO: return bio_map
        pass

3. Exercise 2 — Hybrid Alignment Agent#

Goal: Align biological geometry with hybrid resonance fields.#

Requirements

• accept bio_map
• call S.channel_fill
• call HybridOps.resonance_bridge
• produce hybrid_layer

Starter Scaffold

class HybridAlignmentAgent:
    def align(self, bio_map):
        # TODO: fill channels
        # TODO: bridge resonance
        # TODO: return hybrid_layer
        pass

4. Exercise 3 — Mineral Domain Agent#

Goal: Generate mineral lattice domains from hybrid layer.#

Requirements

• call P.front_propagate
• call M.domain_memory
• produce mineral_map

Starter Scaffold

class MineralDomainAgent:
    def crystallize(self, hybrid_layer):
        # TODO: propagate lattice
        # TODO: encode domain memory
        # TODO: return mineral_map
        pass

5. Exercise 4 — Envelope Advisor Agent#

Goal: Recommend envelope values for each regime.#

Requirements

• BGR moisture: 0.55–0.65
• HRR ion saturation: 0.65–0.75
• MLR supersaturation: ≥ 0.85

Starter Scaffold

class EnvelopeAdvisorAgent:
    def advise(self):
        # TODO: propose BGR envelopes
        # TODO: propose HRR envelopes
        # TODO: propose MLR envelopes
        # TODO: return envelope plan
        pass

6. Exercise 5 — Memory Transfer Agent#

Goal: Move memory through substrate layers.#

Requirements

• call M.route_memory (bio)
• call HybridOps.memory_transfer (hybrid)
• call M.domain_memory (mineral)

Starter Scaffold

class MemoryTransferAgent:
    def transfer(self, bio_map, hybrid_layer):
        # TODO: route memory
        # TODO: transfer memory
        # TODO: encode domain memory
        pass

7. Exercise 6 — Full CME Simulation Agent#

Goal: Integrate all substrate transitions.#

Pipeline

1. BiologicalTraceAgent
2. HybridAlignmentAgent
3. MineralDomainAgent
4. EnvelopeAdvisorAgent
5. MemoryTransferAgent

Starter Scaffold

class CMESimulationAgent:
    def run(self):
        # TODO: run biological stage
        # TODO: run hybrid stage
        # TODO: run mineral stage
        # TODO: compute envelopes
        # TODO: transfer memory
        # TODO: return final CME state
        pass

8. Exercise 7 — Teaching Agent#

Goal: Produce student‑facing explanations.#

Requirements

• run CME simulation
• generate 3–5 conceptual questions
• print both simulation output + questions

Starter Scaffold

class TeachingAgent:
    def lesson(self):
        # TODO: run CME simulation
        # TODO: generate questions
        # TODO: print lesson output
        pass

9. Capstone — Multi‑Agent CME Pipeline#

Goal: Combine all agents into a single workflow.#

Pipeline

• observe biological geometry
• align hybrid resonance
• crystallize mineral lattice
• compute envelopes
• transfer memory
• generate teaching output

Starter Scaffold

def cme_pipeline():
    # TODO: instantiate agents
    # TODO: run each stage
    # TODO: print final results
    pass