Microsoft Agent Framework

One of the challenges I faced developing an agent for my side project (Biotrackr) was how do I manage identity. Some AI Agents share the same service principals or managed identity with the application, which is used to authenticate API calls, access databases etc. This is an issue, because if the application has contributor access to a database, so does the agent. If the agent gets compromised, then the blast radius extends to the entire application’s permission scope. ...

In my side project (Biotrackr), I have a chat agent that I use to query my data using natural language. This agent has 12 tools that call APIs to retrieve data that provides context to a LLM. I’m using Claude as my LLM provider, so Claude will decide which tool to call, and with what parameters. Let’s pretend that we are bad actors trying to disrupt my agent. Say we decide to prompt inject the agent, and get it to perform an expensive query to retrieve 100 years of data (I’m not that old thankfully!) in an attempt to return a massive payload, consume thousands of Claude API tokens, and hammer my APIM gateway. ...

Can your AI Agent run code? If not, you probably don’t think that unexpected code execution applies to you. However, this goes a lot deeper than eval(). Input validation, container security, static analysis, and runtime monitoring all play a part here. Even an Agent with read-only capabilities and no code interpreter has an execution environment, tool parameters that flow from LLM output, and a CI/CD pipeline that needs to be secure. ...

Using the Microsoft Agent Framework, we can build agents that interact with our data via chat capabilities. In my personal project, I decided to create a Chat API that allows me to query my data via a chat interface using an LLM. I wasn’t keen on using OpenAI, or even provisioning Microsoft Foundry to create a deployment so that I could use an LLM that they provide. I decided to just grab an API key for Anthropic so that I could use Claude, and hook it up into my agent so I wouldn’t have to worry about managing any Foundry infrastructure. ...

Chat history and memory allow agents to maintain context across conversations and remember user preferences, which enables agents to provide personalized experiences. Using the Microsoft Agent Framework, we can use in-memory chat message stores, persistent databases, and specialized memory services to cater to a variety of different use cases. In this article, I’ll show you a simple example of how we can use an Azure Cosmos DB Vector store to store conversations we have with an agent, and how we can retrieve conversations so that our agents can maintain context. ...