Update ARCHITECTURE.md

ARCHITECTURE.md

@@ -1,8 +1,6 @@
-<h1 align="center">AISA Architecture</h1>
+<h1 align="center">AISA Reference Architecture</h1>
 
-
-
-AISA models agentic AI systems as composed systems in which behavior emerges from the interaction between reasoning, execution, infrastructure, evaluation, and governance.
+AISA defines agentic AI systems as **composed, governed systems** whose behavior emerges from the interaction between reasoning, execution, infrastructure, evaluation, and policy enforcement.
 
 ---
 
@@ -13,12 +11,13 @@ AISA models agentic AI systems as composed systems in which behavior emerges fro
 </p>
 
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+
 <h2 align="center">Layer Responsibilities</h2>
 
 ---
 
 ### LLM Foundation Layer
-Responsible for core language modeling and inference capabilities.
+Core language modeling, inference, and reasoning substrate.
 
 - Tokenization and inference  
 - Prompt engineering and instruction tuning  
@@ -28,17 +27,17 @@ Responsible for core language modeling and inference capabilities.
 ---
 
 ### Tool & Environment Layer
-Defines how agents interact with external systems and execution environments.
+Controlled interaction with external systems and execution environments.
 
 - Structured tool definitions and schemas  
 - Code execution and sandboxing  
-- Safe function calling and MCP support  
+- Safe function calling and Multi-Call Protocol (MCP) support  
 - Error handling, retries, and permission control  
 
 ---
 
 ### Cognitive Agent Layer
-Implements goal-directed reasoning and decision-making.
+Goal-directed reasoning, planning, and decision-making.
 
 - Task planning and decomposition  
 - Memory management and reflection loops  
@@ -48,7 +47,7 @@ Implements goal-directed reasoning and decision-making.
 ---
 
 ### Agentic Infrastructure Layer
-Provides orchestration and coordination for agent execution.
+Orchestration, coordination, and runtime control.
 
 - Workflow orchestration and coordination  
 - Multi-agent communication patterns  
@@ -58,7 +57,7 @@ Provides orchestration and coordination for agent execution.
 ---
 
 ### Evaluation & Feedback Layer
-Ensures continuous assessment of agent behavior and quality.
+Continuous assessment of behavior, quality, and safety.
 
 - Component-level and behavioral evaluations  
 - Monitoring, metrics, and error analysis  
@@ -68,7 +67,7 @@ Ensures continuous assessment of agent behavior and quality.
 ---
 
 ### Development & Deployment Layer
-Manages lifecycle, experimentation, and system evolution.
+Lifecycle management and controlled system evolution.
 
 - Version control of agents and artifacts  
 - CI/CD pipelines and deployment strategies  
@@ -78,14 +77,14 @@ Manages lifecycle, experimentation, and system evolution.
 ---
 
 ### Governance, Ethics & Policy Layer
-Defines system-wide constraints, oversight, and accountability.
+System-wide constraints, oversight, and accountability.
 
 - AI policies and transparency standards  
 - Fairness, bias mitigation, and privacy protection  
 - Human-in-the-loop governance frameworks  
 - Regulatory compliance and ethical oversight  
----
 
+---
 
 ## Architectural Principles
 
@@ -95,17 +94,22 @@ Defines system-wide constraints, oversight, and accountability.
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 </p>
 
-
 ---
 
-**1. Separation of Concerns**  :Clear separation between system responsibilities to prevent mixing reasoning, permissions, and orchestration logic.
+**1. Separation of Concerns**  
+Clear separation between reasoning, execution, orchestration, and governance responsibilities.
 
-**2. Assurance-by-Design**  :Evaluation, monitoring, and governance are built into the architecture from the start, not added after deployment.
+**2. Assurance-by-Design**  
+Evaluation, monitoring, and governance are embedded into the system architecture from the outset.
 
-**3. Dual-Plane Design**  :A clear distinction between the data plane (runtime execution) and the control plane (policies, permissions, and budgets).
+**3. Dual-Plane Design**  
+A strict distinction between the data plane (runtime execution) and the control plane (policies, permissions, and budgets).
 
-**4. Contract-Driven Interfaces**  :Structured and machine-checkable interfaces reduce ambiguity and improve testing and auditability.
+**4. Contract-Driven Interfaces**  
+Structured, machine-checkable interfaces that reduce ambiguity and improve testability and auditability.
 
-**5. Continuous Improvement Loop**  :Agent behavior evolves continuously through feedback-driven updates to prompts, tools, and policies.
+**5. Continuous Improvement Loop**  
+Agent behavior evolves through feedback-driven updates to prompts, tools, evaluations, and policies.
 
-**6. Practical Deployability**  :The architecture accounts for real-world constraints such as cost, latency, observability, access control, and versioning.
+**6. Practical Deployability**  
+Explicit consideration of real-world constraints including cost, latency, observability, access control, and versioning.