Why Collecting User Feedback on Your AI Agent Actually Matters

You’ve built an AI agent, published it, and users are chatting away. But do you know if it’s actually helping them or quietly frustrating them at scale?

AI agents are only as good as the knowledge they’re built on, and the only way to know where that knowledge breaks down is to ask the people living with the agent every day: your users. Collecting structured feedback isn’t a “nice to have” it’s the feedback loop that separates a good agent from a great one.

The case for structured user feedback

Without feedback, you’re flying blind. You can look at session counts and deflection rates, but those numbers won’t tell you why a user abandoned a conversation, or that the agent confidently gave the wrong answer three days in a row. Structured reactions — thumbs up, thumbs down, and optional comments that give you a direct signal tied to a specific response.

Feedback goes to your organization, not to Microsoft. You own the data and can use it to retrain, refine knowledge sources, or escalate to human agents entirely on your own terms.

Copilot Studio makes it simple to turn it on Preview

Microsoft Copilot Studio ships a built-in User Feedback feature — currently in preview that handles the entire thumbs-up / thumbs-down collection flow for you. Here’s how to enable it in three steps.

1. Open your Agent in Copilot Studio

From the left nav, click the Agents icon to navigate to your agent.

2. Go to Settings → Generative AI

Click the gear to open Settings, then select the Generative AI tab from the sidebar.

3. Toggle “Collect user reactions” to On

Under the User Feedback section, flip the toggle. Optionally add a disclaimer that users see before submitting feedback.

Fig 1: Enabling User Feedback in Copilot Studio Settings › Generative AI (Preview feature)

What you can do with the data

Once feedback is live, Copilot Studio’s Analytics area surfaces everything in a structured, filterable view. The User questions page lets you browse every question asked over the specific date range, filter by response quality, reactions, comments, and knowledge source and immediately spot patterns.

Fig 2: The User Questions page filtered to “Response quality: Irrelevant” — a fast way to find where the agent is falling short

In the example above, five questions answered with “Poor” quality responses are immediately visible. Each row shows the knowledge source the agent drew from — so you can go fix that source directly, not just patch a symptom.

The Reactions page goes one level deeper. Click any response to open its Response details panel — you’ll see the exact user query, the agent’s verbatim response, and the thumbs-down comment the user left. No guesswork, no interpolation.

Fig 3: Drilling into a reaction to see exactly what the agent said and why the user was dissatisfied

In this example, the user asked about disputing a credit card charge — but the agent responded with weather information. The thumbs-down comment confirms what happened: the agent misunderstood the intent entirely. Without this feedback loop, that mismatch could go unnoticed for weeks.

Tracking progress at a glance

The top-level Reactions widget in your Analytics dashboard gives you the aggregate picture: total reactions, thumbs-up vs. thumbs-down split, and week-over-week change. When you improve a knowledge source after a wave of negative feedback, this is where you see whether it worked.

Fig 4 : The Reactions widget: 933 total reactions, 94.2% positive. The 55% week-over-week lift shows improvement is measurable.

A 94.2% thumbs-up rate with a +55% week-over-week increase tells a clear story: the agent is delivering value and trending in the right direction. These are the numbers you can take to stakeholders as evidence that your AI investment is working.

Every unanswered question or misunderstood prompt is a moment your agent failed a real person. User feedback collection turns those invisible failures into a prioritized queue of improvements. Copilot Studio’s preview feature makes the setup frictionless — the harder part is committing to acting on what you learn.

Turn it on. Watch the data. Fix the gaps. Repeat.

To learn more details, you can check Microsoft Learn documentation: Analyze conversational agent effectiveness — Microsoft Copilot Studio | Microsoft Learn