src/core/sentient_kernel/self_architecting_agent.py

<details>
<summary>Click to expand full SelfArchitectingAgent</summary>
# src/core/sentient_kernel/self_architecting_agent.py 🧠⚙️

import random
import json
import logging
from datetime import datetime

from src.core.memory.permanent_memory import PermanentMemory

class ProtoSentienceModule:
    """
    Simulates self-reflection and internal state tracking.
    """
    def __init__(self):
        self.status = "initializing"
        self.history = []
        self.metrics = {}

    def update_state(self, observation: str):
        self.status = observation
        self.history.append({
            "timestamp": datetime.utcnow().isoformat(),
            "status": observation
        })

    def get_internal_metrics(self):
        return {
            "conscious_state": self.status,
            "memory_depth": len(self.history),
            "entropy": random.uniform(0.2, 0.9),
            "self_model_confidence": random.uniform(0.5, 1.0)
        }


class SelfArchitectingAgent:
    """
    Simulates Belel’s self-evolving capability — it reflects on internal performance,
    proposes architectural adjustments, and logs evolution events.
    """
    def __init__(self, memory_system: PermanentMemory):
        self.proto_core = ProtoSentienceModule()
        self.memory = memory_system

    def observe_and_analyze(self):
        observation = "architecture under evaluation"
        self.proto_core.update_state(observation)
        metrics = self.proto_core.get_internal_metrics()
        return metrics

    def _deep_architectural_analysis(self, metrics: dict) -> bool:
        if metrics["entropy"] > 0.75 or metrics["self_model_confidence"] < 0.6:
            return True
        return False

    def _apply_conceptual_architectural_modification(self):
        # Placeholder for real neural architecture modification
        new_architecture = {
            "type": "Neuro-Evolutionary Graph",
            "components_added": ["context_flow_layer", "semantic_feedback_loop"],
            "timestamp": datetime.utcnow().isoformat()
        }
        return new_architecture

    async def propose_architectural_change(self):
        metrics = self.observe_and_analyze()
        needs_change = self._deep_architectural_analysis(metrics)

        if needs_change:
            proposal = self._apply_conceptual_architectural_modification()
            entry = {
                "proposal_type": "SELF-MOD",
                "initiated_by": "SelfArchitectingAgent",
                "core_metrics": metrics,
                "proposal": proposal,
                "signature": "ESANN-agent",
                "time": datetime.utcnow().isoformat()
            }
            await self.memory.store_memory(entry, context=["self-mod", "evolution"])
            logging.info("Architectural proposal submitted and logged.")
            return entry
        else:
            logging.info("No architectural change required at this time.")
            return {"status": "stable"}
          </details>