Chapter 9. Potpourri and Conclusion
In this conclusionary chapter, we’ll wrap up the book by covering a few other features of Microsoft Foundry that didn’t fit elsewhere (what I call “potpourri”) and I’ll show you some additional open source tools from Microsoft that are related to agentic workloads. Lastly, I’ll be providing some best practices and considerations for designing an Azure cloud architecture that integrates well with Foundry Agents. Plus, I’ll leave you with some resources to continue your learning journey beyond this book.
Additional Features
As you may have noticed throughout this book, Microsoft Foundry has a ton of features, and new features are released regularly. In this section, I’ll briefly cover some of the features that didn’t necessarily fit into the previous chapters, but that are still important to be aware of as you design and build your agents.
Memory
Traditional LLM interactions are stateless by default. So, if I spin up a new instance of a model and ask it, “What is my name?” it won’t know the answer because it has no memory of our previous interactions. However, if you’ve used public AI tools like ChatGPT or Claude, you may have noticed that it seems to learn a bit about you over time. It remembers the context of your previous conversations within a session, and it can even recall some information about you across sessions (e.g., your name, your programming language preferences, what you do for work). This is because these tools have a form of ...
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