Power Automate AI Assistant with Foundry

Key insights

• Stop parameter
  The video shows how the stop parameter controls where a model ends its reply, preventing runaway or unwanted text. It’s a key tool when you design prompts and want predictable assistant behavior.
• Power Automate flow
  The demo builds a practical flow that saves messages, reads history, and uses that context to answer new prompts. Each run appends the latest input and returns a context-aware response.
• SharePoint memory
  A SharePoint list serves as the assistant’s persistent memory, storing past messages so the bot remembers prior interactions and keeps conversation continuity.
• Roles: User, Assistant, System
  The video explains role usage: the system role sets behavior, user messages ask questions, and assistant messages form replies—organizing these improves prompt engineering.
• Azure AI Foundry
  Foundry connects models, tools, and orchestration to Power Automate, enabling agent-native workflows, model routing, and tighter integration with Microsoft 365 data.
• Key advantages
  The solution offers simpler development, enterprise-grade scalability, built-in security and governance, and cost-aware model routing for efficient automation.

Andrew Hess of MySPQuestions recently published a practical YouTube walkthrough that shows how to build an AI assistant using Power Automate together with Microsoft Foundry. In the video, he demonstrates a working flow that turns a SharePoint list into a simple memory system so the assistant can reference past messages. Consequently, the demo highlights prompt engineering details and why the stop parameter matters when controlling model responses. Overall, the video targets Power Platform developers who want a clear, hands-on example of connecting automation, memory, and AI models.

Overview of the Demo

The video opens with a concise explanation of the goals and then walks viewers through a step-by-step build. First, Hess sets up roles like user and assistant and explains the flow of data between Power Automate and Microsoft Foundry. Then he shows a live run where the assistant answers a practical question about Thanksgiving, which makes the example easy to follow. As a result, viewers see both the mechanics and the user experience in a single demo.

Hess also breaks the video into clear chapters, which helps viewers jump to specific topics such as the stop parameter, system role design, and connecting to data. For instance, he spends time demonstrating how to append conversation history to a string variable and how to test different prompt formats. Moreover, the inclusion of testing and alternative prompt types gives a fuller picture of real-world usage. Therefore, the video functions as both tutorial and reference.

How the Flow Works

In the core of the tutorial, Hess builds a Power Automate flow that saves each message to a SharePoint list, retrieves the stored history, and then sends that history back to the model as context. This approach creates a persistent memory so the assistant can use prior exchanges to shape its response. The flow writes the new message, pulls everything back from the list, and formats a combined prompt for the model. As a result, the assistant produces replies that reflect continuity rather than responding in isolation.

Hess also highlights the practical steps required to connect services, such as granting permissions and configuring connectors in Power Automate. He demonstrates using string variables to assemble conversation history and how to include system and role-based prompts to frame the assistant’s behavior. By walking through these details, he makes the integration accessible to developers who already know the Power Platform. Thus, the tutorial reduces barriers to trying out AI-enabled automations in a business setting.

Understanding the stop parameter

A central teaching point in the video is the role of the stop parameter and why it matters for prompt structure. Hess explains that by setting stop tokens you can control where the model should end its output, which prevents unintended content or runaway generation. He then shows examples where different stop values change how the assistant replies, demonstrating the parameter’s practical impact. Consequently, learning to apply the stop parameter became one of the most actionable takeaways.

Moreover, Hess places the stop parameter in context with roles like system, user, and assistant, explaining how the model interprets these inputs. He argues that careful prompt design combined with stop rules yields more predictable results, especially when the flow appends memory entries. Nevertheless, he also notes that rigid stop patterns can truncate useful content if not chosen carefully. Therefore, tuning requires testing and a balance between strictness and flexibility.

Tradeoffs and Implementation Challenges

The video candidly covers tradeoffs that developers face when building persistent assistants, such as memory size versus response speed. For example, pulling long histories from SharePoint improves context but increases latency and may raise costs when models process more tokens. Conversely, trimming history reduces latency but risks losing essential context that helps deliver accurate and personalized answers. Thus, Hess encourages iterative testing to find the right balance for a given scenario.

He also highlights operational challenges like error handling, permission scopes, and data governance, which grow in importance as assistants access sensitive business data. While the demo focuses on a small-scale example, Hess emphasizes that production deployments require stronger identity management, monitoring, and retention policies. Furthermore, integrating with enterprise systems introduces latency and complexity that developers must design around. Therefore, teams must weigh convenience against security and maintainability.

Implications for Developers

For Power Platform developers, the video offers a concrete pattern to experiment with: store conversation snippets in SharePoint, compose them into a prompt in Power Automate, and let Microsoft Foundry or Azure models generate context-aware responses. Importantly, Hess shows that you do not need deep infrastructure skills to get started, but you do need careful prompt engineering and governance. As a result, the approach can accelerate small proofs of concept while signaling the work required to scale.

In closing, the tutorial serves as a useful bridge between learning and practical application, providing replicable steps and clear caveats. Developers can use the patterns shown to prototype assistants that retain context and behave predictably when guided by the stop parameter and role prompts. Ultimately, the video demonstrates both the promise and the limits of simple memory systems, encouraging teams to experiment while planning for broader operational needs.

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