adding Reference Architectures section

2_design_patterns/README.md

@@ -1,39 +1,39 @@
-## Agentic Design Patterns
-
-### [Pattern 01 - Retrieval Augmented Generation (RAG) Agents](01-rag-agent/README.md)
-
-RAG Agents are designed to retrieve information from a multitude of sources, including databases, documents, and APIs, and then use this information to generate an answer.
-
-This pattern will focus on Retrieval from unstructured data, which comes with a lot of challenges including the need to understand the context to deliver on accuracy, performance and cost of operation.
-
-We put RAG in perspective within the context of an Agentic AI System, to showcase how complex problems can be solved by a RAG enabled agent.
-
-:rocket: [Retrieval Augmented Generation (RAG) Accelerator](../3_accelerators/01-rag-agent/README.md)
-
-### [Pattern 02 - Code Generation Agents](02-code-generation-agent/README.md)
-
-This pattern covers the design and implementation of agents that can generate code snippets, functions, or even entire programs based on a set of requirements. This pattern is particularly useful for developers who want to increase their productivity while respecting existing code standards, libraries, reference architectures, and best practices.
-
-The applicability of a Code Generation Agent is vast, and we will cover the most common scenarios, namely:
-- coding assistance
-- code translation
-- automated testing
-
-:rocket: [Code Generation Agent Accelerator](../3_design_patterns_accelerators/02-code-generation-agent/README.md)
-
-### [Pattern 03 - Multi-Domain Agents](03-multi-domain-agents/README.md)
-
-This pattern explores best practices for building Multi-Agent Systems (MAS) that consist of multiple agents. Each agent focuses on a specific functional area, and they are coordinated to combine their capabilities, offering a unified, enhanced super-agent experience to the end user. The primary goal is to scale the agent system across multiple functional domains. The pattern will address several key aspects of MAS, including:  
-
-- Decomposition of the overall system's functional scope into more manageable segments.  
-- Designing mechanisms for routing and forwarding.  
-- Management of shared memory and state transitions across agents.  
-- Handling the transition of communication flow from one agent to another.  
-
-They also enable pausing tasks in one domain to switch to another domain and then resume the task in the original domain if needed to complete a complex workflow, or if directed by the user.
-
-The pattern will cover some key-aspects of this pattern, including:
-- domain scoping & detection, domain switching, request delegation
-- long-running contextual memory: memorizing context accross domains to be able to resume task started in domain A when user switches to domain B and then back to domain A
-
-:rocket: [Agentic Analytics Accelerator](../3_accelerators/03-multi-domain-agents/automating_analytics/README.md)
+## Agentic Design Patterns
+
+### [Pattern 01 - Retrieval Augmented Generation (RAG) Agents](01-rag-agent/README.md)
+
+RAG Agents are designed to retrieve information from a multitude of sources, including databases, documents, and APIs, and then use this information to generate an answer.
+
+This pattern will focus on Retrieval from unstructured data, which comes with a lot of challenges including the need to understand the context to deliver on accuracy, performance and cost of operation.
+
+We put RAG in perspective within the context of an Agentic AI System, to showcase how complex problems can be solved by a RAG enabled agent.
+
+:rocket: [Retrieval Augmented Generation (RAG) Accelerator](../4_accelerators/01-rag-agent/README.md)
+
+### [Pattern 02 - Code Generation Agents](02-code-generation-agent/README.md)
+
+This pattern covers the design and implementation of agents that can generate code snippets, functions, or even entire programs based on a set of requirements. This pattern is particularly useful for developers who want to increase their productivity while respecting existing code standards, libraries, reference architectures, and best practices.
+
+The applicability of a Code Generation Agent is vast, and we will cover the most common scenarios, namely:
+- coding assistance
+- code translation
+- automated testing
+
+:rocket: [Code Generation Agent Accelerator](../4_accelerators/02-code-generation-agent/README.md)
+
+### [Pattern 03 - Multi-Domain Agents](03-multi-domain-agents/README.md)
+
+This pattern explores best practices for building Multi-Agent Systems (MAS) that consist of multiple agents. Each agent focuses on a specific functional area, and they are coordinated to combine their capabilities, offering a unified, enhanced super-agent experience to the end user. The primary goal is to scale the agent system across multiple functional domains. The pattern will address several key aspects of MAS, including:  
+
+- Decomposition of the overall system's functional scope into more manageable segments.  
+- Designing mechanisms for routing and forwarding.  
+- Management of shared memory and state transitions across agents.  
+- Handling the transition of communication flow from one agent to another.  
+
+They also enable pausing tasks in one domain to switch to another domain and then resume the task in the original domain if needed to complete a complex workflow, or if directed by the user.
+
+The pattern will cover some key-aspects of this pattern, including:
+- domain scoping & detection, domain switching, request delegation
+- long-running contextual memory: memorizing context accross domains to be able to resume task started in domain A when user switches to domain B and then back to domain A
+
+:rocket: [Agentic Analytics Accelerator](../4_accelerators/03-multi-domain-agents/automating_analytics/README.md)