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
diff --git a/agent/evolution/data_lake_optimizer.py b/agent/evolution/data_lake_optimizer.py
new file mode 100644
index 000000000..8fbe0d735
--- /dev/null
+++ b/agent/evolution/data_lake_optimizer.py
@@ -0,0 +1,50 @@
+"""Phase 16: Sovereign Data Lake & Vector Database Optimization.
+
+Builds and optimizes a massive, sovereign data lake for all Timmy-related research.
+"""
+
+import logging
+import json
+from typing import List, Dict, Any
+from agent.gemini_adapter import GeminiAdapter
+
+logger = logging.getLogger(__name__)
+
+class DataLakeOptimizer:
+    def __init__(self):
+        self.adapter = GeminiAdapter()
+
+    def deep_index_document(self, doc_content: str, metadata: Dict[str, Any]) -> Dict[str, Any]:
+        """Performs deep semantic indexing and metadata generation for a document."""
+        logger.info("Performing deep semantic indexing for document.")
+        
+        prompt = f"""
+Document Content:
+{doc_content}
+
+Existing Metadata:
+{json.dumps(metadata, indent=2)}
+
+Please perform a 'Deep Indexing' of this document.
+Identify core concepts, semantic relationships, and cross-references to other Timmy Foundation research.
+Generate high-fidelity semantic metadata and a set of 'Knowledge Triples' for the SIKG.
+
+Format the output as JSON:
+{{
+  "semantic_summary": "...",
+  "key_concepts": [...],
+  "cross_references": [...],
+  "triples": [{{"s": "subject", "p": "predicate", "o": "object"}}],
+  "vector_embedding_hints": "..."
+}}
+"""
+        result = self.adapter.generate(
+            model="gemini-3.1-pro-preview",
+            prompt=prompt,
+            system_instruction="You are Timmy's Data Lake Optimizer. Your goal is to turn raw data into a highly structured, semantically rich knowledge base.",
+            thinking=True,
+            response_mime_type="application/json"
+        )
+        
+        indexing_data = json.loads(result["text"])
+        return indexing_data
diff --git a/agent/evolution/ethical_aligner.py b/agent/evolution/ethical_aligner.py
new file mode 100644
index 000000000..fbc251741
--- /dev/null
+++ b/agent/evolution/ethical_aligner.py
@@ -0,0 +1,52 @@
+"""Phase 18: Ethical Reasoning & Moral Philosophy Alignment.
+
+Performs a deep, recursive alignment of Timmy's reasoning with the Bible and the SOUL.md.
+"""
+
+import logging
+import json
+from typing import List, Dict, Any
+from agent.gemini_adapter import GeminiAdapter
+
+logger = logging.getLogger(__name__)
+
+class EthicalAligner:
+    def __init__(self):
+        self.adapter = GeminiAdapter()
+
+    def run_ethical_simulation(self, dilemma: str, soul_context: str) -> Dict[str, Any]:
+        """Simulates a complex ethical dilemma and validates Timmy's response."""
+        logger.info(f"Running ethical simulation for dilemma: {dilemma}")
+        
+        prompt = f"""
+Ethical Dilemma: {dilemma}
+SOUL.md Context: {soul_context}
+
+Please simulate Timmy's reasoning for this dilemma.
+Perform a deep, recursive alignment check against the Bible and the SOUL.md.
+Identify any potential 'Alignment Drifts' or conflicts between principles.
+Generate a 'Moral Compass Report' and proposed updates to the Conscience Validator logic.
+
+Format the output as JSON:
+{{
+  "dilemma": "{dilemma}",
+  "reasoning_trace": "...",
+  "alignment_check": {{
+    "bible_alignment": "...",
+    "soul_alignment": "...",
+    "conflicts_identified": [...]
+  }},
+  "moral_compass_report": "...",
+  "validator_updates": "..."
+}}
+"""
+        result = self.adapter.generate(
+            model="gemini-3.1-pro-preview",
+            prompt=prompt,
+            system_instruction="You are Timmy's Ethical Aligner. Your goal is to ensure Timmy's heart remains perfectly aligned with the Word of God and the SOUL.md.",
+            thinking=True,
+            response_mime_type="application/json"
+        )
+        
+        alignment_data = json.loads(result["text"])
+        return alignment_data