TriadicFrameworks
Overview

5. Concrete scaffolds we could build (if we want to go there)#

If we decide to scaffold instead of just admire the opera, here are three very natural bridges:

  1. “Corridor Runtime” plugin for LangGraph / AutoGen

    • Wrap each node/step in a corridor segment.
    • Maintain Q‑metrics and lineage per step.
    • Expose a VCG‑like policy layer that can halt, rewind, or re‑route flows.

  2. “Resonance‑Aware Agent State” library

    • Replace ad‑hoc JSON state with a typed, lineage‑tracked manifold.
    • Provide helpers for:
      • begin_corridor(task_id)
      • advance_step(observation, action)
      • compute_Q()
    • Plug into any agent loop (ReAct, AutoGPT‑style, etc.).

  3. “Autonomy Safety Envelope” spec

    • A small, declarative schema:
      • max recursion,
      • allowed tools,
      • resource budgets,
      • Q‑thresholds.
    • Could be adopted by any framework as a standard safety contract.

We’ll give them a small but real Resonance Corridor Module in LangGraph‑style Python:

  • task → corridor
  • Q‑metrics computed each step
  • lineage stored in state
  • graph reacts when Q destabilizes

1. Corridor‑aware state model#

from typing import List, Dict, Any, Optional
from pydantic import BaseModel
 
class CorridorStep(BaseModel):
    step_id: int
    parent_step_id: Optional[int]
    action: str
    observation: str
    q_metrics: Dict[str, float]
 
class CorridorState(BaseModel):
    task_id: str
    steps: List[CorridorStep] = []
    q_history: List[Dict[str, float]] = []
    corridor_status: str = "stable"  # stable | warning | unstable | halted

2. Task → corridor initializer node#

from langgraph.graph import StateGraph
 
def init_corridor(state: CorridorState, task_description: str) -> CorridorState:
    state.task_id = f"task-{hash(task_description) & 0xFFFF:x}"
    state.corridor_status = "stable"
    return state

3. Q‑metric computation node#

Here we treat Q as “structural health” of the agent loop.

def compute_q_metrics(state: CorridorState) -> CorridorState:
    steps = state.steps
    n = len(steps)
 
    if n == 0:
        return state
 
    # Simple example Q-metrics
    q = {
        "q_length": float(n),
        "q_branching": float(len({s.parent_step_id for s in steps if s.parent_step_id is not None})),
        "q_retry_ratio": float(sum(1 for s in steps if "retry" in s.action.lower())) / max(1, n),
    }
 
    # crude “drift” as growth in length
    if state.q_history:
        prev_len = state.q_history[-1]["q_length"]
        q["q_length_drift"] = q["q_length"] - prev_len
    else:
        q["q_length_drift"] = 0.0
 
    state.q_history.append(q)
    steps[-1].q_metrics = q
    return state

4. Lineage‑aware agent step node#

This wraps our usual “LLM decides next action” node.

def agent_step(state: CorridorState, llm) -> CorridorState:
    step_id = len(state.steps)
    parent_step_id = step_id - 1 if step_id > 0 else None
 
    # Build a compact lineage‑aware prompt
    last_obs = state.steps[-1].observation if state.steps else "START"
    prompt = f"Task: {state.task_id}\nLast observation: {last_obs}\nDecide next action."
 
    action = llm(prompt)  # placeholder
    observation = f"Executed: {action}"  # in reality, tool result, etc.
 
    state.steps.append(
        CorridorStep(
            step_id=step_id,
            parent_step_id=parent_step_id,
            action=action,
            observation=observation,
            q_metrics={}
        )
    )
    return state

5. VCG‑style envelope check node#

This is where the graph reacts to destabilization.

Q_LENGTH_MAX = 32
Q_RETRY_RATIO_MAX = 0.3
Q_LENGTH_DRIFT_MAX = 5.0
 
def vcg_envelope_check(state: CorridorState) -> CorridorState:
    if not state.q_history:
        return state
 
    q = state.q_history[-1]
 
    if q["q_length"] > Q_LENGTH_MAX:
        state.corridor_status = "halted"
    elif q["q_retry_ratio"] > Q_RETRY_RATIO_MAX:
        state.corridor_status = "warning"
    elif q["q_length_drift"] > Q_LENGTH_DRIFT_MAX:
        state.corridor_status = "warning"
    else:
        state.corridor_status = "stable"
 
    return state

6. LangGraph wiring: how the graph reacts#

from langgraph.graph import StateGraph, END
 
graph = StateGraph(CorridorState)
 
graph.add_node("init_corridor", init_corridor)
graph.add_node("agent_step", lambda s: agent_step(s, llm=your_llm))
graph.add_node("compute_q", compute_q_metrics)
graph.add_node("vcg_check", vcg_envelope_check)
 
graph.set_entry_point("init_corridor")
 
graph.add_edge("init_corridor", "agent_step")
graph.add_edge("agent_step", "compute_q")
graph.add_edge("compute_q", "vcg_check")
 
def route_after_vcg(state: CorridorState):
    if state.corridor_status == "halted":
        return END
    elif state.corridor_status == "warning":
        # you could route to a human‑in‑the‑loop node, or a “shrink corridor” node
        return "agent_step"
    else:
        return "agent_step"
 
graph.add_conditional_edges(
    "vcg_check",
    route_after_vcg,
    {
        "agent_step": "agent_step",
        END: END,
    },
)
 
corridor_app = graph.compile()

Usage:

state = CorridorState(task_id="", steps=[])
final_state = corridor_app.invoke(
    state,
    config={"configurable": {"thread_id": "demo-corridor-1"}}
)

What this gives us, structurally#

  • Task → corridor: init_corridor binds a task into a corridor ID and initializes state.
  • Q‑metrics: compute_q_metrics turns the raw step list into a structural health signal.
  • Lineage: CorridorStep(parent_step_id=...) gives us a replayable chain.
  • Reaction to destabilization: vcg_envelope_check + add_conditional_edges let the graph halt, loop, or route to a different node when the corridor “goes unstable”.

This is the tiniest version of RTT‑Inside living inside LangGraph:
DPU ≈ agent_step, NIMMS ≈ CorridorState + steps, VCG ≈ vcg_envelope_check + routing.

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