From 8d4130153cc30f9470eae40dc15e4658272aa166 Mon Sep 17 00:00:00 2001
From: Google AI Agent <gemini@hermes.local>
Date: Mon, 30 Mar 2026 23:22:42 +0000
Subject: [PATCH] feat: implement Phase 17 - ARD Engine

---
 agent/evolution/ard_engine.py | 49 +++++++++++++++++++++++++++++++++++
 1 file changed, 49 insertions(+)
 create mode 100644 agent/evolution/ard_engine.py

diff --git a/agent/evolution/ard_engine.py b/agent/evolution/ard_engine.py
new file mode 100644
index 000000000..e51750ec2
--- /dev/null
+++ b/agent/evolution/ard_engine.py
@@ -0,0 +1,49 @@
+"""Phase 17: Autonomous Research & Development (ARD).
+
+Empowers Timmy to autonomously propose, design, and build his own new features.
+"""
+
+import logging
+import json
+from typing import List, Dict, Any
+from agent.gemini_adapter import GeminiAdapter
+from tools.gitea_client import GiteaClient
+
+logger = logging.getLogger(__name__)
+
+class ARDEngine:
+    def __init__(self):
+        self.adapter = GeminiAdapter()
+        self.gitea = GiteaClient()
+
+    def run_self_evolution_loop(self, performance_logs: str) -> Dict[str, Any]:
+        """Analyzes performance and identifies areas for autonomous growth."""
+        logger.info("Running autonomous self-evolution loop.")
+        
+        prompt = f"""
+Performance Logs:
+{performance_logs}
+
+Please analyze these logs and identify areas where Timmy can improve or expand his capabilities.
+Generate a 'Feature Proposal' and a 'Technical Specification' for a new autonomous improvement.
+Include the proposed code changes and a plan for automated testing.
+
+Format the output as JSON:
+{{
+  "improvement_area": "...",
+  "feature_proposal": "...",
+  "technical_spec": "...",
+  "proposed_code_changes": [...],
+  "automated_test_plan": "..."
+}}
+"""
+        result = self.adapter.generate(
+            model="gemini-3.1-pro-preview",
+            prompt=prompt,
+            system_instruction="You are Timmy's ARD Engine. Your goal is to autonomously evolve the sovereign intelligence toward perfection.",
+            thinking=True,
+            response_mime_type="application/json"
+        )
+        
+        evolution_data = json.loads(result["text"])
+        return evolution_data