Bridging the Gap: Why Your AI Needs a "Nervous System"

Bridging the Gap: Why Your AI Needs a "Nervous System"

The Problem: Intelligence Evaporates

Most AI agents operate in a vacuum. They solve a task, forget the context, and move on. When they encounter an error or a documentation mismatch, they either hallucinate a workaround or fail silently. In an enterprise setting, this is cognitive leakage.

To build a truly AI-native organization, we need more than just "smart" agents—we need Cognitive Governance.

Introducing the Signal Processor

The Signal Processor is the "Nervous System" of the TeamBrain framework. It acts as the bridge between an Agent’s real-time reasoning and your organization's permanent file system.

Instead of an agent just "knowing" something is wrong, the Signal Processor allows it to programmatically commit that observation as a structured Signal.

The Core Logic: signal_processor.py

We’ve built this script to be lightweight and executable by any LLM with a "Code Interpreter" tool or as a standalone CLI for your dev team. It standardizes how truth is captured before it decays.

import os

import datetime

from pathlib import Path

class TeamBrainSignalProcessor:

def __init__(self, repo_root="."):

self.repo_root = Path(repo_root)

self.signal_dir = self.repo_root / ".teambrain" / "signals"

# Ensure directories exist

self.signal_dir.mkdir(parents=True, exist_ok=True)

def emit_signal(self, title, originator, category, current_assumption, observed_reality, evidence, proposed_change):

"""Creates a standardized Signal file."""

timestamp = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M")

safe_title = title.lower().replace(" ", "_")[:30]

filename = f"SIGNAL_{timestamp}_{safe_title}.md"

filepath = self.signal_dir / filename

template = f"""# 📡 SIGNAL: {title}

## 1. Metadata

- **Originator:** {originator}

- **Timestamp:** {datetime.datetime.now().strftime("%Y-%m-%d")}

- **Category:** {category}

- **Status:** 🟢 Pending Review

## 2. The Observation

- **Current Assumption:** {current_assumption}

- **Observed Reality:** {observed_reality}

## 3. Evidence

{evidence}

## 4. Proposed Truth Injection

- **Change Required:** {proposed_change}

"""

with open(filepath, "w") as f:

f.write(template)

return f"Signal successfully emitted to {filepath}"

How It Works in Production

The Signal Processor transforms your repository into a living organism that learns from its own execution:

* Detection: An AI Agent encounters a contradiction (e.g., "The docs say the API limit is 100, but I’m getting throttled at 50").

* Execution: The Agent calls the signal_processor.py with the observed data.

* Persistence: A standardized .md file is generated in your repo’s .teambrain folder.

* Governance: A human Lead or a GitHub Action reviews the signal, leading to a Truth Injection—updating the core documentation so no agent (or human) makes that mistake again.

What’s Next?

Capturing intelligence is only half the battle. The next step is preventing Knowledge Decay.

Soon, we will introduce the Stale Knowledge Scanner—a specialized tool that identifies "rotting" documentation by comparing your core Truths against the frequency of incoming Signals.

> "Intelligence without governance is entropy. TeamBrain is the cure."

>