Update ARCHITECTURE.md

ARCHITECTURE.md

@@ -16,31 +16,33 @@ AISA models agentic AI systems as composed systems in which behavior emerges fro
 ---
 
 ## Layer Responsibilities
+ 
+---
 
-### Governance, Ethics & Policy Layer
-- AI policies and transparency standards  
-- Fairness and bias mitigation  
-- Privacy and data protection  
-- Accountability frameworks and human-in-the-loop governance  
-- Regulatory compliance and ethical reflection  
+### LLM Foundation Layer
+- Tokenization and inference  
+- Prompt engineering and instruction tuning  
+- LLM APIs and adapters  
+- Context window optimization  
+- Fine-tuning, alignment, and safety grounding  
 
 ---
 
-### Development & Deployment Layer
-- Version control of agents and artifacts  
-- Continuous integration and deployment  
-- Performance benchmarking and A/B testing  
-- Cost–latency trade-off management  
-- Security, access control, and lifecycle tracking  
+### Tool & Environment Layer
+- Tool creation and structured syntax  
+- Code execution and sandboxing  
+- Safe function calling and MCP support  
+- Error handling and retries  
+- Permission, rate control, and structured I/O  
 
 ---
 
-### Evaluation & Feedback Layer
-- Component-level evaluations  
-- Behavioral monitoring and quality metrics  
-- Error analysis and prioritization  
-- Human-in-the-loop evaluations  
-- Automated regression testing  
+### Cognitive Agent Layer
+- Task planning and decomposition  
+- Reflection loops and self-improvement  
+- Memory management  
+- Multi-turn reasoning and goal tracking  
+- Integration of external feedback  
 
 ---
 
@@ -53,33 +55,34 @@ AISA models agentic AI systems as composed systems in which behavior emerges fro
 
 ---
 
-### Cognitive Agent Layer
-- Task planning and decomposition  
-- Reflection loops and self-improvement  
-- Memory management  
-- Multi-turn reasoning and goal tracking  
-- Integration of external feedback  
+### Evaluation & Feedback Layer
+- Component-level evaluations  
+- Behavioral monitoring and quality metrics  
+- Error analysis and prioritization  
+- Human-in-the-loop evaluations  
+- Automated regression testing  
 
 ---
 
-### Tool & Environment Layer
-- Tool creation and structured syntax  
-- Code execution and sandboxing  
-- Safe function calling and MCP support  
-- Error handling and retries  
-- Permission, rate control, and structured I/O  
+### Development & Deployment Layer
+- Version control of agents and artifacts  
+- Continuous integration and deployment  
+- Performance benchmarking and A/B testing  
+- Cost–latency trade-off management  
+- Security, access control, and lifecycle tracking  
 
 ---
 
-### LLM Foundation Layer
-- Tokenization and inference  
-- Prompt engineering and instruction tuning  
-- LLM APIs and adapters  
-- Context window optimization  
-- Fine-tuning, alignment, and safety grounding  
+### Governance, Ethics & Policy Layer
+- AI policies and transparency standards  
+- Fairness and bias mitigation  
+- Privacy and data protection  
+- Accountability frameworks and human-in-the-loop governance  
+- Regulatory compliance and ethical reflection  
 
 ---
 
+
 ## Architectural Principles
 
 <p align="center">