cognitive-cell 0.9.4

A context-sensitive cognitive control stack for workflow AI.

Cognitive Cell

Cognitive Cell is a context-sensitive control stack for workflow AI.

Accepted v9 stack:

router-v4 → selector-v5 → finalizer-v9

What it does

The system separates:

1. cognitive routing
2. workflow-vs-direct pathway selection
3. final user-facing rendering

This lets the same input behave differently depending on context, posture, urgency, role, and workflow constraints.

Install

pip install "cognitive-cell[server]"

Python usage

from cognitive_cell import CognitiveCellRequest, CognitiveCellV9

cell = CognitiveCellV9()

request = CognitiveCellRequest(
    statement="Blue colour is observed.",
    interaction_mode="workflow_component",
    autonomy_mode="log",
)

result = cell.run(request)

print(result.response_text)
print(result.trace)

CLI usage

Create an event JSON file, then run:

cognitive-cell --event-json examples/event.example.json

This calls the configured model and may incur API cost.

HTTP sidecar usage

Start the server:

python -m uvicorn cognitive_cell.server.app:app --port 8000

Check health without model calls:

curl -s http://127.0.0.1:8000/health

Send an enterprise event:

curl -s -X POST http://127.0.0.1:8000/v1/sidecar \
  -H "Content-Type: application/json" \
  -d @examples/event.example.json

Example event

{
  "event_id": "evt_pricing_refunds_001",
  "source": "growth_ops_monitor",
  "event_type": "metric_anomaly",
  "statement": "Refund requests doubled after the pricing page update. What should we examine first?",
  "context": {
    "world_facts": [],
    "constraints": ["Prioritize high-signal first checks before broad analysis."],
    "active_goals": ["identify the first diagnostic step"]
  },
  "metadata": {
    "persona": "growth operations analyst",
    "time_pressure": "medium"
  },
  "interaction_mode": "workflow_component",
  "autonomy_mode": "suggest"
}

Current evidence

Fresh holdout-v1, 100 cases:

Judge	Architecture preference	Baseline preference
gpt-4.1 primary	0.6200	0.3800
gpt-5.5 second, combined 40+60	0.5575	0.4425
Two-judge mean	0.58875	0.41125

Safe claim:

On a fresh 100-case holdout, the frozen v9 cognitive-cell stack beat a plain strong-model baseline under two standardized OpenAI judges, with mean architecture preference around 0.589.

Caution

This is an engineering validation result, not a universal claim of superiority over frontier models. Larger benchmarks, human evaluation, ablations, and cross-provider validation are still needed.

Cost note

/health costs nothing.

/v1/sidecar and cognitive-cell --event-json ... call the configured model and may incur API cost.

Recommended production posture

Start with:

autonomy_mode = "suggest"
human-in-the-loop
no automatic external action execution

Known weaknesses

• Atomic observation remains weaker because pure logging competes against advice/explanation.
• Contextual observation remains mixed when direct action beats record/analyze behavior.
• Persona shift is weaker under the second judge.
• Writing support is improved but not consistently superior.

What this is not

Cognitive Cell is not AGI, not a production-autonomous agent, and not a claim of universal superiority over frontier models.

It is a workflow-control layer that helps decide whether to record, clarify, analyze, plan, answer directly, or escalate.