Copilot Studio: Read Excel with Agents

Key insights

• Prepare Excel as Table
  Format your spreadsheet as an Excel table with clear column headers and no merged cells. Store the file in OneDrive or SharePoint so Copilot Studio can access it.
• Connect via Data Connectors
  In Copilot Studio create an agent or topic and add the Excel file as a data source. Confirm the agent has the right permissions to read the file.
• Use Power Fx for Queries
  Use simple Power Fx expressions to filter and calculate data, for example Filter(table, condition) and Sum(table[Column]). Power Fx keeps queries low-code and easy to adjust.
• Common Read Errors and Fixes
  Fix unreadable formats, missing headers, merged cells, or wrong ranges to avoid errors. Also check file permissions and connector settings when data won’t load.
• Test and Refine Agent Prompts
  Test the agent with realistic questions like “total sales for Q1.” Refine prompts to specify tables, ranges, or date filters to improve accuracy.
• Use Cases and Benefits
  Agents turn spreadsheets into conversational tools for tasks like sales reports, data Q&A, and simple analytics. They offer live access, low-code queries, and enterprise permission control.

Overview

Griffin Lickfeldt (Citizen Developer) published a clear how-to YouTube video that explains how to read Excel files with AI agents built in Microsoft Copilot Studio. The video walks viewers through connecting spreadsheets, configuring agent flows, and testing outputs so agents can return meaningful answers from tabular data. In short, the guide targets builders who want agents to query tables stored in cloud locations and respond conversationally with calculations or filtered results. Consequently, this article summarizes the video’s practical steps, highlights tradeoffs, and notes common challenges for teams adopting this approach.

How the Video Demonstrates Connecting Excel

Lickfeldt begins by showing how to prepare an Excel workbook for agent access, emphasizing the need to format data as a proper table and include clear headers. Then, he demonstrates linking that workbook in Copilot Studio through cloud storage like OneDrive or SharePoint, which preserves access controls and keeps data current. He also outlines building a topic and adding actions that use Power Fx expressions to filter rows, compute sums, and return specific columns as part of a conversational flow. As a result, viewers can see how a static spreadsheet becomes an interactive backend for a Copilot agent.

Practical Benefits and Use Cases

The video makes clear that integrating Excel with agents delivers immediate productivity gains for non-technical users who prefer chat over manual spreadsheet work. For instance, teams can ask an agent for weekly sales totals, filter records by date, or run quick lookups without writing custom APIs or full applications. Moreover, because the method uses platform-native connectors and Power Fx, builders can implement common data operations with less code while retaining enterprise storage and permission models. Consequently, this approach fits well for internal tools, reports, and Q&A scenarios where speed and simplicity matter most.

Challenges and Tradeoffs

Despite the benefits, Lickfeldt points out several tradeoffs that teams must weigh carefully before adopting agents as a primary data interface. First, correct table formatting is essential; poorly structured sheets cause errors or incorrect outputs, so time must be invested in data hygiene and consistent headers. Second, using cloud storage helps with permissions but introduces potential latency and access control complexity, so balancing convenience with security is crucial. Finally, while Power Fx simplifies many tasks, it can limit advanced transformations that would be easier in a full scripting environment, forcing builders to choose between low-code speed and high-code flexibility.

Testing, Debugging, and Deployment

Lickfeldt demonstrates how to test agents by simulating user queries and inspecting the agent flow outputs, which helps identify where prompts or expressions need refinement. He emphasizes iteration: adjust the prompt to be specific, check that filters target the right table, and validate calculations against the original spreadsheet to avoid surprises. In addition, he shows deployment options such as publishing the agent to Teams or the web, noting that testers should verify both permissions and performance in the production channel. Thus, a repeatable testing loop reduces the risk of incorrect answers when agents serve real users.

Final Takeaways

Overall, Griffin Lickfeldt’s video offers a practical, step-by-step method for turning Excel files into interactive data sources via Copilot agents in Microsoft Copilot Studio. The tutorial balances accessibility with caution: it highlights simple wins for citizen developers while warning about data preparation, permission management, and limits of low-code logic. Ultimately, organizations that invest in clean tables, proper testing, and thoughtful security can make conversational agents a valuable addition to their data toolkit. Therefore, this guide serves as a useful starting point for anyone aiming to extract immediate value from spreadsheets using AI agents.

• Video timeline: Intro; Reading Excel Data; Setup Agent Flow; Get Excel Outputs; Setup Agent Prompt; Agent Flow Outputs; Setup Agent in Copilot Studio; Testing The Agent; Subscribe.

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