Why are we doing this?
Customers and internal teams often need a fast, low-complexity starting point for GPT‑RAG: a single-agent pattern that is easy to deploy, reason about, and extend later—without immediately jumping into multi-agent orchestration. A clean baseline also helps us standardize guidance and reduce “design drift” across implementations.
This aligns with GPT‑RAG’s goal of accelerating enterprise GenAI assistant delivery with an enterprise-ready, secure foundation.
What does it do?
Creates a reference strategy + implementation blueprint for a Simple RAG, Single Agent architecture using Microsoft Agent Framework as the orchestration layer, grounded on Azure AI Search (classic RAG or agentic retrieval where appropriate). The output should be a practical “baseline package” (diagram + decisions + minimal components) that teams can implement and expand.
Why are we doing this?
Customers and internal teams often need a fast, low-complexity starting point for GPT‑RAG: a single-agent pattern that is easy to deploy, reason about, and extend later—without immediately jumping into multi-agent orchestration. A clean baseline also helps us standardize guidance and reduce “design drift” across implementations.
This aligns with GPT‑RAG’s goal of accelerating enterprise GenAI assistant delivery with an enterprise-ready, secure foundation.
What does it do?
Creates a reference strategy + implementation blueprint for a Simple RAG, Single Agent architecture using Microsoft Agent Framework as the orchestration layer, grounded on Azure AI Search (classic RAG or agentic retrieval where appropriate). The output should be a practical “baseline package” (diagram + decisions + minimal components) that teams can implement and expand.