Update ARCHITECTURE.md

ARCHITECTURE.md

@@ -14,55 +14,80 @@ AISA models agentic AI systems as composed systems in which behavior emerges fro
 
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 <h2 align="center">Layer Responsibilities</h2>
- 
+
+
+The AISA architecture is composed of layered responsibilities, progressing from model-level execution to system-wide governance. Each layer has a 
+clearly defined role and scope.
+
+---
+
 ### LLM Foundation Layer
+Responsible for core language modeling and inference capabilities.
+
 - Tokenization and inference  
 - Prompt engineering and instruction tuning  
-- LLM APIs and adapters  
-- Context window optimization  
-- Fine-tuning, alignment, and safety grounding  
+- LLM APIs, adapters, and context window management  
+- Alignment, safety grounding, and fine-tuning  
+
 ---
+
 ### Tool & Environment Layer
-- Tool creation and structured syntax  
+Defines how agents interact with external systems and execution environments.
+
+- Structured tool definitions and schemas  
 - Code execution and sandboxing  
 - Safe function calling and MCP support  
-- Error handling and retries  
-- Permission, rate control, and structured I/O  
+- Error handling, retries, and permission control  
+
 ---
+
 ### Cognitive Agent Layer
+Implements goal-directed reasoning and decision-making.
+
 - Task planning and decomposition  
-- Reflection loops and self-improvement  
-- Memory management  
+- Memory management and reflection loops  
 - Multi-turn reasoning and goal tracking  
-- Integration of external feedback  
+- Integration of external and human feedback  
+
 ---
+
 ### Agentic Infrastructure Layer
+Provides orchestration and coordination for agent execution.
+
 - Workflow orchestration and coordination  
 - Multi-agent communication patterns  
-- State coordination and observability  
-- Logging, monitoring, and dashboards  
-- Latency, cost optimization, and EvalOps pipelines  
+- State management and observability  
+- Logging, monitoring, and cost–latency optimization  
+
 ---
+
 ### Evaluation & Feedback Layer
-- Component-level evaluations  
-- Behavioral monitoring and quality metrics  
-- Error analysis and prioritization  
-- Human-in-the-loop evaluations  
-- Automated regression testing  
+Ensures continuous assessment of agent behavior and quality.
+
+- Component-level and behavioral evaluations  
+- Monitoring, metrics, and error analysis  
+- Human-in-the-loop evaluation  
+- Automated regression and quality testing  
+
 ---
+
 ### Development & Deployment Layer
+Manages lifecycle, experimentation, and system evolution.
+
 - 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  
+- CI/CD pipelines and deployment strategies  
+- Benchmarking, A/B testing, and performance tracking  
+- Security, access control, and lifecycle management  
+
 ---
+
 ### Governance, Ethics & Policy Layer
+Defines system-wide constraints, oversight, and accountability.
+
 - 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  
+- Fairness, bias mitigation, and privacy protection  
+- Human-in-the-loop governance frameworks  
+- Regulatory compliance and ethical oversight  
 ---