Cosmos DB

Let's integrate AI Agents in Event-Sourced Systems — Divakar Kumar, FlyersSoft

Jul 30, 2026 · 21:37

Divakar Kumar explains how to layer AI agents onto event-sourced systems to resolve ambiguous fraud cases that rule-based engines and ML models cannot score. In his architecture, bounded contexts (transaction, device, account) feed events through change feeds into a semantic layer that agents read asynchronously via a message broker in a saga-style loop. A risk analyzer agent and a behavior analyzer agent fan out, each using tools to query the semantic layer, then a third verdict agent synthesizes their outputs to decide whether to approve or block a transaction. Kumar emphasizes keeping memory short and guarding against infinite loops to meet sub-500ms SLAs. The key takeaway: event sourcing already carries the state and history an agent needs, so the cleanest way to add judgment is to layer agents onto the events you already emit.

RAG at scale: production ready GenAI apps with Azure AI Search

Feb 13, 2025 · 21:53

Pablo, a Distinguished Engineer at Microsoft, explains how Azure AI Search enables production-grade RAG applications at scale by addressing key scaling challenges: data volume, query load, workflow complexity, and diverse data types. He demonstrates the platform's hybrid retrieval combining vector search (HNSW, exhaustive), keyword search (BM25), and filters, with a two-stage system using cross-encoder re-ranking for improved quality, achieving 100ms re-ranking latency. Azure AI Search supports multi-billion vector indexes through 10–12x storage limit increases and quantization (int8, single-bit) preserving 90–95% precision, with oversampling for full-precision re-ranking. Integrated vectorization automates ingestion from Azure sources (blob, Cosmos DB, OneLake) with change tracking and format handling (PDFs, Office documents, images). OpenAI uses Azure AI Search to back ChatGPT file uploads and the Assistants API, scaling user limits 500x after Microsoft's infrastructure upgrades.

Build, Evaluate and Deploy a RAG-Based Retail Copilot with Azure AI: Cedric Vidal and David Smith

Feb 6, 2025 · 1:57:58

David Smith, Cedric Vidal, and Miguel Martinez lead a hands-on workshop on building a production-level RAG-based retail copilot using Azure AI. They demonstrate how to build a chatbot backend that retrieves product information from Azure AI Search via vector embeddings and customer history from Cosmos DB, then augments the LLM prompt to generate grounded answers. The session covers using Azure AI Studio and Prompt flow to orchestrate the RAG workflow, deploying the flow as a managed endpoint, and evaluating quality with GPT-4 as a judge on metrics like relevance and groundedness. The speakers also explain the LLM Ops lifecycle for iterative improvement and compare Prompt flow with Semantic Kernel and AutoGen.