Uploaded June 2026 | Updated September 2026, 1 week ago
The Azure Cosmos DB Agent Kit is now generally available — bringing years of Cosmos DB engineering expertise directly into GitHub Copilot. One command to install. Zero context-switching.
In this video, we show how the Agent Kit:
• Detects and fixes SDK anti-patterns (like creating a new CosmosClient per request)
• Recommends partition key designs with clear tradeoffs
• Suggests composite indexing strategies based on your actual query patterns
This isn't generic AI code generation — it's expert-level Cosmos DB guidance built into your editor.
🚀 Get started:
⏱️ Timestamps:
0:00 — Introduction
0:10 — What is the Agent Kit?
0:30 — Demo: Fixing SDK anti-patterns
0:50 — Demo: Partition key design
1:00 — Demo: Indexing optimization
1:10 — Wrap-up
🔗 Links:
• Agent Kit repo: github.com/AzureCosmosDB/cosmosdb-agent-kit
• Azure Kit docs : learn.microsoft.com/azure/cosmos-db
#AzureCosmosDB #GitHubCopilot #AgentKit #AI #DatabaseDevelopment #Azure
The Azure Cosmos DB Agent Kit is now generally available — bringing years of Cosmos DB engineering expertise directly into GitHub Copilot. One command to install. Zero context-switching.
In this video, we show how the Agent Kit:
• Detects and fixes SDK anti-patterns (like creating a new CosmosClient per request)
• Recommends partition key designs with clear tradeoffs
• Suggests composite indexing strategies based on your actual query patterns
This isn't generic AI code generation — it's expert-level Cosmos DB guidance built into your editor.
🚀 Get started:
⏱️ Timestamps:
0:00 — Introduction
0:10 — What is the Agent Kit?
0:30 — Demo: Fixing SDK anti-patterns
0:50 — Demo: Partition key design
1:00 — Demo: Indexing optimization
1:10 — Wrap-up
🔗 Links:
• Agent Kit repo: github.com/AzureCosmosDB/cosmosdb-agent-kit
• Azure Kit docs : learn.microsoft.com/azure/cosmos-db
#AzureCosmosDB #GitHubCopilot #AgentKit #AI #DatabaseDevelopment #Azure








![Multi-Agent API with LangGraph and Azure Cosmos DB
The rise of multi-agent AI applications is transforming how we build intelligent systems - but how do you architect them for real-world scalability and performance? In this session, we’ll take a deep dive into a production-grade multi-agent application built with LangGraph for agent orchestration, FastAPI for an API layer, and Azure Cosmos DB as the backbone for state management, vector storage, and transactional data.
Through a detailed code walkthrough, you’ll see how to design and implement an agent-driven workflow that seamlessly integrates retrieval-augmented generation (RAG), memory persistence, and dynamic state transitions. We’ll cover:
- Agent collaboration with LangGraph for structured reasoning
- Real-time chat history storage using Azure Cosmos DB - the same database that powers the chat history in ChatGPT, the fastest-growing AI agent application in history
- Vector search for knowledge retrieval with Cosmos DBs native embeddings support
- FastAPI’s async capabilities to keep interactions responsive and scalable
By the end of this session, you’ll have a clear blueprint for building and deploying your own scalable, cloud-native multi-agent applications that harness the power of modern AI and cloud infrastructure. Whether youre an AI engineer, cloud architect, or Python developer, this talk will equip you with practical insights and battle-tested patterns to build the next generation of AI-powered applications
#MicrosoftReactor #learnconnectbuild #AgentHack
📌 Learn more about the series here: https://aka.ms/AgentHack-Py/y
[eventID:25314] Multi-Agent API with LangGraph and Azure Cosmos DB](https://i.ytimg.com/vi/RTYI-j0QVIE/mqdefault.jpg)

