Copilot Analyst Agent: Analyze Data and Check the Results

The Copilot Analyst agent in Microsoft 365 Copilot analyzes data you provide, running code, creating charts, and returning a structured interpretation. Using it well means framing the analysis question clearly before you start, reading the method it used as carefully as the result, and recording the assumptions that the team approved so they do not disappear when the session ends. Tools like fenbs keep those approved assumptions and the next checks in one place for everyone on the team.

An analysis is only as good as the question it answers. Getting the framing right before you hand the data to the agent saves a round of corrections and makes the result easier to check.

What the Copilot Analyst Agent does

The Analyst is a specialized agent in Microsoft 365 Copilot for tasks that involve structured data. It can execute Python code to process a file, produce summary statistics, build charts, and explain its method. It is distinct from the Researcher agent, which focuses on multi-source information gathering. For that side of the work, see the Copilot Researcher agent workflow. Both agents can appear in the same multi-agent workflow when a project needs research and analysis in sequence.

• Code execution to process structured data files such as spreadsheets.
• Summary statistics: totals, averages, distributions, and trend lines.
• Chart generation to visualize the data alongside the written interpretation.
• Step-by-step explanation of the method it used, which you can check and correct.

How to frame an analysis task

Before uploading any data, write out the question the analysis must answer. A clear question limits what the agent optimizes for and makes it easier to spot when the result does not actually address what you needed.

1. State the question in one sentence. "Which product category had the highest return rate in the third quarter, and was that rate higher or lower than the previous quarter?" is actionable. "Analyze the sales data" is not.
2. Name the metric and the comparison explicitly. If you want a percentage rather than a raw count, say so.
3. List any filters upfront: the time range, the geography, the segment. Do not leave the agent to infer them from the file name.
4. Describe what you already know. If you know one data field is unreliable, say so before the analysis starts.
5. Ask it to show its working. Requesting the code or the calculation steps lets you verify the logic before presenting the finding.

Checking the output

A chart or a summary number can look authoritative even when the underlying calculation is wrong. Build checking into your workflow rather than treating the output as final.

• Check the row and column counts. If the agent filtered or excluded rows, confirm it was for the right reason.
• Reproduce one number by hand. Pick a total or a percentage from the output and check it against the raw data.
• Read the method explanation. If the agent averaged values that should be summed, or summed values that should be averaged, the result is wrong regardless of how confident the text sounds.
• Check the chart axes. A chart that starts at a non-zero baseline can make a small change look large.

Recording approved assumptions

Most analyses rest on assumptions: which rows to exclude, how to handle missing values, which time zone the timestamps use. When the team agrees on those, record the decisions so the next analyst, person or AI, starts from the same foundation.

An assumption that lives only in a chat session is invisible to every future session. A decision recorded in a shared place becomes a rule the next analyst reads before touching the data.

How to do this with fenbs

1. Create a free account and add a project for the analysis work.
2. Record agreed data assumptions in Decisions and rules: which rows are excluded, how missing values are handled, the approved time range. Note who approved each one.
3. Add outstanding checks as tasks in To Do: a specific finding to verify manually, an edge case to investigate, a chart to review with a stakeholder.
4. Connect a supported AI app over MCP from Integrations. At the start of the next session, ask it to read the project context before running any analysis.
5. When the analysis is complete and reviewed, update the tasks to Completed and note the final interpretation so the next person does not re-run work that is already settled.

What to watch for

The Analyst agent is well suited to analysis tasks where the data and the question are clear. It is a weaker fit when the question itself needs clarification, when the data is unstructured text rather than a table, or when the result will be used to make a significant financial or operational decision without independent review. In those cases, use it to draft the method and the charts, and have a person check the calculation before presenting the finding. Keep a note of which analysis tasks required significant correction. That record helps you decide when the agent saves time and when doing the work manually is faster.