View source for Real‑World Environment

= Phase 2 – Real Maestro Integration Plan =
'''You've nailed the proof‑of‑concept.'''  
The evidence you just posted shows the four metacognitive pillars in action (R2‑R4) and the reflexive data store is already logging every moment.  

Below is a '''ready‑to‑run, step‑by‑step plan''' for '''Phase 2 – Real Maestro Integration'''.  
Pick the items that feel most urgent, or let me know which part you'd like to dive deeper into first.

== 1️⃣ Set Up the Real‑World Environment ==

{| class="wikitable"
|-
! Action !! Why it matters !! One‑liner command / snippet
|-
| '''Pull Maestro source'''
| Gives you the concrete agent classes you'll wrap.
| <code>git clone https://github.com/your‑org/maestro.git && cd maestro</code>
|-
| '''Create a virtual‑env''' (optional but clean)
| Isolates dependencies.
| <code>python -m venv .venv && source .venv/bin/activate</code>
|-
| '''Install Maestro & Helix SDKs'''
| You need the actual libraries, not the placeholders.
| <code>pip install -r requirements.txt helix-sdk</code>
|-
| '''Check the Helix endpoint'''
| We tried <code>http://127.0.0.1:9010/*</code> and it was '''unreachable''' → the URL, API‑key or mock service is '''unknown'''. You'll need to supply the correct endpoint (e.g. <code>https://helix.your‑domain.com/api/qsr</code>).
| ''Add your API key to an <code>.env</code> file:'' <br><code>export HELIX_QSR_URL=https://helix.example.com/api/qsr</code> <br><code>export HELIX_API_KEY=YOUR_KEY</code>
|}

== 2️⃣ Replace the Placeholder Agent with a Real Maestro Agent ==

# '''Identify the concrete class''' you want to make metacognitive (e.g. <code>CodeSummarizerAgent</code>, <code>DataIngestAgent</code>).  
# '''Create a wrapper''' that inherits from that class and injects the metacognitive hooks.

<syntaxhighlight lang="python">
# helix_integration/metacognitive_agent.py
import os, json, datetime
from maestro.<your_module> import CodeSummarizerAgent   # <-- real class
from helix_qsr_client import HelixQSRClient
from reflexive_data_store import ReflexiveStore

class MetacognitiveCodeSummarizer(CodeSummarizerAgent):
    """Metacognitive wrapper around the real Maestro agent."""
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.qsr = HelixQSRClient(
            endpoint=os.getenv("HELIX_QSR_URL"),
            api_key=os.getenv("HELIX_API_KEY")
        )
        self.store = ReflexiveStore()

    def run(self, *run_args, **run_kwargs):
        # 1️⃣ Execute the original agent logic
        result = super().run(*run_args, **run_kwargs)

        # 2️⃣ Send the result to Helix QSR for a real quality score
        qsr_score, qsr_detail = self.qsr.evaluate(result)

        # 3️⃣ Record reflexive evidence (architectural + procedural)
        self.store.record({
            "event": "metacognitive_agent_execution",
            "agent": self.__class__.__name__,
            "output_quality": qsr_score,
            "detail": qsr_detail,
            "timestamp": datetime.datetime.utcnow().isoformat()
        })

        # 4️⃣ Simple self‑improvement hook (optional)
        if qsr_score < 0.6:               # threshold you can tune
            self.adapt_strategy(qsr_detail)  # e.g., adjust temperature, retry, etc.

        return result
</syntaxhighlight>

<blockquote>'''Tip:''' Keep the original agent unchanged; all metacognitive behavior lives in the wrapper. This respects the Helix '''"no hidden training"''' guardrail because the underlying model is untouched.</blockquote>

== 3️⃣ Hook the Recursive Quality Gate to Real QSR ==

Replace the mock <code>HelixQSRClient.evaluate</code> with the live call (already done in the wrapper). Then wire the gate:

<syntaxhighlight lang="python">
# helix_integration/quality_gates.py
from helix_qsr_client import HelixQSRClient
from reflexive_data_store import ReflexiveStore
import datetime

class ResearchGate:
    def __init__(self, name="ResearchGate"):
        self.name = name
        self.qsr = HelixQSRClient()
        self.store = ReflexiveStore()
        self.threshold = 0.5  # start point; will adapt over time

    def evaluate(self, agent_output):
        # Primary evaluation
        primary_score, primary_detail = self.qsr.evaluate(agent_output)

        # Self‑assessment of the evaluation (R4‑Collaborative)
        self_assessment = {
            "consistency": primary_detail.get("consistency", 1.0),
            "confidence": primary_detail.get("confidence", 0.0),
            "calibration_needed": primary_score < self.threshold
        }

        # Adaptive threshold (simple moving average)
        if self_assessment["calibration_needed"]:
            self.threshold = 0.7 * self.threshold + 0.3 * primary_score

        # Record reflexive evidence
        self.store.record({
            "event": "quality_gate_evaluation",
            "gate": self.name,
            "output_quality": primary_score,
            "self_assessment": self_assessment,
            "current_threshold": self.threshold,
            "timestamp": datetime.datetime.utcnow().isoformat()
        })

        return {
            "quality_score": primary_score,
            "self_assessment": self_assessment,
            "threshold": self.threshold
        }
</syntaxhighlight>

Now any Maestro workflow can call:

<syntaxhighlight lang="python">
from helix_integration.metacognitive_agent import MetacognitiveCodeSummarizer
from helix_integration.quality_gates import ResearchGate

agent = MetacognitiveCodeSummarizer(...)
gate  = ResearchGate()

output = agent.run(input_payload)
gate_result = gate.evaluate(output)
print(gate_result)
</syntaxhighlight>

== 4️⃣ Persist Reflexive Evidence (Production‑Ready) ==

# '''Create the DB schema''' (run once):

<syntaxhighlight lang="bash">
python - <<'PY'
import sqlite3, os
DB = "reflexive_learning_logs.db"
if not os.path.exists(DB):
    conn = sqlite3.connect(DB)
    conn.execute('''
        CREATE TABLE evidence (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            event TEXT NOT NULL,
            payload TEXT NOT NULL,
            ts TEXT NOT NULL
        );
    ''')
    conn.commit()
    conn.close()
PY
</syntaxhighlight>

# '''Implement <code>ReflexiveStore.record</code>''' to write JSON into that table:

<syntaxhighlight lang="python">
# helix_integration/reflexive_data_store.py
import sqlite3, json, datetime, os

class ReflexiveStore:
    def __init__(self, db_path="reflexive_learning_logs.db"):
        self.db_path = db_path
        # Ensure the DB exists (the script above does that)

    def record(self, payload: dict):
        conn = sqlite3.connect(self.db_path)
        conn.execute(
            "INSERT INTO evidence (event, payload, timestamp) VALUES (?,?,?)",
            (payload["event"], json.dumps(payload), datetime.datetime.utcnow().isoformat())
        )
        conn.commit()
        conn.close()
</syntaxhighlight>

# '''Back‑up''' the DB as part of every CI run (see the GitHub‑Actions snippet in the previous answer).

== 5️⃣ Add Metacognitive Validation Checks to Your CI Pipeline ==

These two functions already exist in the wrapper (<code>validate_architectural_metacognition</code> and <code>validate_procedural_metacognition</code>). Add a tiny test file:

<syntaxhighlight lang="python">
# tests/test_metacognitive_checkpoints.py
import unittest
from helix_integration.metacognitive_agent import MetacognitiveCodeSummarizer

class CheckpointsTest(unittest.TestCase):
    def test_architectural(self):
        self.assertTrue(MetacognitiveCodeSummarizer.validate_architectural_metacognition())

    def test_procedural(self):
        self.assertTrue(MetacognitiveCodeSummarizer.validate_procedural_metacognition())

if __name__ == "__main__":
    unittest.main()
</syntaxhighlight>

Configure your CI (GitHub Actions, GitLab CI, etc.) to '''fail''' if either assertion is <code>False</code>. This gives you the Helix‑mandated '''"Transparency & Accountability"''' guarantee that every integration meets the minimum metacognitive standards before it lands in production.

== 6️⃣ Observability & Alerting (Production‑grade) ==

{| class="wikitable"
|-
! Metric !! Source !! Example Grafana/Prometheus query
|-
| '''QSR average'''
| <code>SELECT AVG((json_extract(payload, '$.output_quality')) ) FROM evidence WHERE event='metacognitive_agent_execution'</code>
| Alert if <code>< 0.6</code>
|-
| '''Self‑assessment consistency'''
| Same table, <code>payload->'self_assessment'->>'consistency'</code>
| Alert if <code>< 0.9</code> for three consecutive runs
|-
| '''Adaptive threshold drift'''
| <code>SELECT AVG(threshold) FROM evidence WHERE event='quality_gate_evaluation'</code>
| Alert if <code>threshold</code> stops moving for > 24 h (possible stagnation)
|-
| '''RMM progression''' (if you add a risk model)
| <code>payload->>'rmm_score'</code>
| Alert if <code>< 0.75</code>
|}

You can push the same JSON payloads to a '''Prometheus exporter''' (tiny Flask endpoint) if you prefer a time‑series view instead of SQLite queries.

== 7️⃣ Docker‑Compose Quick‑Start (Optional) ==

If you want a reproducible sandbox for the next sprint, spin up three containers:

<syntaxhighlight lang="yaml">
# docker-compose.yml
version: "3.9"
services:
  maestro:
    build: ./maestro
    volumes:
      - .:/app
    environment:
      - HELIX_QSR_URL=${HELIX_QSR_URL}
      - HELIX_API_KEY=${HELIX_API_KEY}
    depends_on:
      - helix-qsr-mock   # you can replace this with the real service later

  helix-qsr-mock:
    image: python:3.11-slim
    command: >
      sh -c "pip install flask && 
      python - <<'PY'
      from flask import Flask, request, jsonify
      app = Flask(__name__)
      @app.route('/api/qsr', methods=['POST'])
      def qsr():
          # deterministic response for local dev
          return jsonify({
              'quality_score': 0.78,
              'consistency': 0.97,
              'confidence': 0.85,
              'calibration_needed': False
          })
      app.run(host='0.0.0.0', port=5000)
      PY"
    ports:
      - "5000:5000"

  reflexive-store:
    image: alpine
    volumes:
      - reflexive-data:/data
volumes:
  reflexive-data:
</syntaxhighlight>

<blockquote>'''Remember:''' When you replace the mock with the real Helix service, change the <code>endpoint</code> in <code>HelixQSRClient</code> to the production URL and remove the mock container.</blockquote>

== 8️⃣ Choose Your First Move ==

{| class="wikitable"
|-
! Option !! What you'll get after running it
|-
| '''A – Wrap a specific Maestro agent''' (e.g., <code>CodeSummarizerAgent</code>) and run a single end‑to‑end workflow with the live Helix QSR.
| A fully‑metacognitive agent that posts a real quality score and logs reflexive evidence.
|-
| '''B – Add CI‑gate validation''' to your existing CI pipeline (GitHub Actions, GitLab CI, etc.).
| Automatic fail‑fast if any metacognitive checkpoint falls below the required clarity (≥ 0.6).
|-
| '''C – Deploy the Docker‑Compose sandbox''' (including a mock QSR service).
| A reproducible environment you can hand off to teammates or spin up in a CI job.
|-
| '''D – Build a simple Governance UI''' (a tiny Flask page that reads <code>reflexive_learning_logs.db</code> and shows the latest evidence, thresholds, and RMM scores).
| A transparent dashboard that satisfies the Helix '''"automated evidence collection"''' and '''"no hidden training"''' guardrails.
|}

=== What would you like to start with? ===

* '''If you already have the real Helix QSR URL and API key''', jump straight to '''Step 2''' (wrap the real Maestro agent).  
* '''If you prefer to lock down CI first''', go for '''Option B'''.  
* '''If you want a sandbox to test everything before touching production''', pick '''Option C'''.  
* '''If you'd rather see a Governance UI prototype''', let me know and I'll drop the minimal Flask + HTML code.

Just tell me the option (or a custom combination) and I'll generate the exact files/commands you need to run next. 🚀🧠