6. Optional: Stream JSON traces live

If we want real‑time UI updates:

def stream_trace_update(state, websocket):
    update = {
        "step": len(state.steps) - 1,
        "q": state.q_history[-1],
        "status": state.corridor_status
    }
    websocket.send(json.dumps(update))

This gives us a live corridor monitor.

Here we go, Nawder — this is the moment where our agent‑corridor runtime gets its own file format, just like hardware engineers have VCD, FSDB, or ETW.
Except this one is for reasoning corridors, not voltage transitions.

Below is a clean, extensible, future‑proof Corridor Trace File (CTF) specification — something we can log, replay, diff, visualize, and even stream.

We’ll give:

• the file format
• the schema
• the event model
• the encoding rules
• and a minimal writer

This is the first real “waveform dump” for agent cognition.

Corridor Trace File (CTF) — Specification v0.1#

A CTF file is a structured, append‑only log of a corridor execution.
It is designed to be:

• streamable (line‑delimited JSON events)
• indexable (step‑indexed)
• lineage‑aware
• Q‑metric rich
• tool‑agnostic
• visualization‑friendly

Think of it as:
VCD + Chrome DevTools trace + LangGraph state + Q‑metric physics.

1. File Structure#

A .ctf file is a sequence of newline‑delimited JSON objects, each representing an event.

{ "type": "header", ... }
{ "type": "step", ... }
{ "type": "q", ... }
{ "type": "rewind", ... }
{ "type": "status", ... }
{ "type": "footer", ... }

This makes it:

• easy to stream
• easy to parse incrementally
• easy to visualize live

2. Event Types#

2.1. header#

Metadata about the corridor run.

{
  "type": "header",
  "task_id": "task-9af3",
  "timestamp": 1736280000.123,
  "corridor_spec": {
    "max_steps": 64,
    "max_semantic_drift": 0.35,
    "max_tool_entropy": 2.0,
    "max_latency_drift": 1.0,
    "max_branching_pressure": 8,
    "max_retry_ratio": 0.25
  }
}

2.2. step#

Represents a single agent action + observation.

{
  "type": "step",
  "step_id": 12,
  "parent_step_id": 11,
  "timestamp": 1736280002.551,
  "action": "search('quantum turbulence')",
  "observation": "Executed search",
  "tool": "search",
  "latency_ms": 412
}

2.3. q#

Q‑metrics computed after each step.

{
  "type": "q",
  "step_id": 12,
  "semantic_drift": 0.22,
  "tool_entropy": 1.58,
  "latency_drift": 0.14,
  "branching_pressure": 3,
  "retry_ratio": 0.08
}

2.4. status#

Corridor‑level status update.

{
  "type": "status",
  "step_id": 12,
  "corridor_status": "warning",
  "violations": ["semantic_drift"]
}

2.5. rewind#

Lineage‑based rollback event.

{
  "type": "rewind",
  "from_step": 12,
  "to_step": 10,
  "timestamp": 1736280002.900
}

2.6. footer#

Marks the end of the trace.

{
  "type": "footer",
  "timestamp": 1736280003.200,
  "final_status": "stable"
}

3. Schema Summary#

4. Encoding Rules#

4.1. Line‑delimited JSON (LDJSON)#

Each event is a single JSON object on its own line.

4.2. Append‑only#

Events are written in chronological order.

4.3. Step‑indexed#

step_id is monotonically increasing unless a rewind occurs.

4.4. Rewind semantics#

After a rewind event:

• future steps are considered invalid
• the viewer should visually “snap back”
• Q‑metrics after the rewind are recomputed

4.5. Deterministic replay#

A viewer can reconstruct the entire corridor by replaying events in order.

5. Minimal CTF Writer#

Add this to our corridor runtime:

import json
 
class CTFWriter:
    def __init__(self, file_path):
        self.f = open(file_path, "w")
 
    def write(self, event: dict):
        self.f.write(json.dumps(event) + "\n")
        self.f.flush()
 
    def close(self):
        self.f.close()

6. Integrating with the corridor runtime#

6.1. Emit header#

writer.write({
    "type": "header",
    "task_id": state.task_id,
    "timestamp": time.time(),
    "corridor_spec": self.spec.dict()
})

6.2. Emit step#

writer.write({
    "type": "step",
    "step_id": step.step_id,
    "parent_step_id": step.parent_step_id,
    "timestamp": step.timestamp,
    "action": step.action,
    "observation": step.observation,
    "tool": extract_tool(step.action),
    "latency_ms": step.latency_ms
})

6.3. Emit Q‑metrics#

writer.write({
    "type": "q",
    "step_id": step.step_id,
    **step.q_metrics
})

6.4. Emit status#

writer.write({
    "type": "status",
    "step_id": step.step_id,
    "corridor_status": state.corridor_status,
    "violations": violations
})

6.5. Emit rewind#

writer.write({
    "type": "rewind",
    "from_step": old_step,
    "to_step": new_step,
    "timestamp": time.time()
})

6.6. Emit footer#

writer.write({
    "type": "footer",
    "timestamp": time.time(),
    "final_status": state.corridor_status
})

7. What this unlocks#

With .ctf files, we can now build:

A Corridor Waveform Viewer#

• Q‑metric waveforms
• branching graph
• semantic drift heatmap
• rewind markers
• tool‑use entropy timeline

A Live Corridor Monitor#

• stream events over WebSockets
• visualize stability envelopes in real time

A Debugger#

• step through lineage
• inspect Q‑metrics
• replay corridor evolution

A Diff Tool#

• compare two corridor runs
• highlight divergence points
• compute drift deltas

This is the agent equivalent of VCD, Chrome trace, and OpenTelemetry rolled into one.

We’re literally building the VCD viewer for cognition—love this.

We’ll give just enough to be real and droppable into triadicframeworks.org later:

• A React/Next.js Corridor Viewer page that loads a .ctf JSON (or API) and renders:
  • step timeline
  • status badges
  • Q‑metric panel shell
• A D3.js Q‑metric waveform panel component that we can plug into that viewer

I’ll assume Next.js 13+ / App Router, TypeScript, and a simple /api/corridor/:id that returns parsed CTF as JSON.