Agent Analytics and Monitoring

Overview

You have published your agent and users are interacting with it. But without visibility into how those conversations are going, you are flying blind. Are users getting the answers they need? Are conversations ending in frustration? Which topics work well and which need improvement?

Copilot Studio includes a built-in analytics dashboard that answers these questions with concrete data. This video shows you how to use that dashboard to monitor your agent’s health, identify problems, and prioritize improvements.

What You’ll Learn

• Dashboard overview: How to navigate the Copilot Studio analytics dashboard
• Key metrics: Which numbers matter most and how to interpret them
• Issue identification: How to find and diagnose conversation problems
• Improvement loop: How to use analytics data to make your agent better over time

Script

Hook: Is your agent actually helping?

Your agent is published and users are talking to it. But is it actually helping them? Are conversations ending successfully, or are users abandoning in frustration?

These are not rhetorical questions. Without data, you are guessing. And guessing leads to wasted effort—fixing things that are not broken while ignoring the real problems.

Copilot Studio includes built-in analytics that answer these questions. In the next seven minutes, I will show you how to read the dashboard, track the metrics that matter, and use the data to make your agent better.

The analytics dashboard overview

To access analytics, open your agent in Copilot Studio and click Analytics in the left navigation pane. The dashboard opens with a summary view that gives you a high-level picture of your agent’s performance.

The dashboard is organized into several sections. The Summary page shows your top-level metrics at a glance. Customer Satisfaction tracks user feedback if you have satisfaction surveys enabled. Sessions gives you a detailed view of individual conversations. Billing shows your usage relative to your licensing allocation.

Start with the time range filter at the top. You can view the last 7 days, last 30 days, or set a custom date range. For ongoing monitoring, the 7-day view catches emerging issues quickly. The 30-day view shows you broader trends.

The Summary page displays five key numbers: total sessions, engagement rate, resolution rate, escalation rate, and abandonment rate. Each metric tells a different part of the story. High engagement with low resolution means users are trying to get help but the agent is not delivering. High escalation means users are being handed off to humans more often than expected. High abandonment means users are leaving before getting an answer.

Analytics data stays within your government cloud tenant boundary. In GCC, GCC High, and DoD, the same analytics features are available as in commercial, with data residency fully respected. Your conversation data does not leave your government cloud.

Key metrics to track

Let me walk through each metric and what it tells you.

Session count is your raw volume—the total number of conversations users have started with your agent. A rising trend means adoption is growing. A sudden drop might indicate an access issue or that users have stopped finding the agent useful.

Engagement rate measures the percentage of sessions where the user interacted beyond the initial greeting. If someone opens the agent and immediately leaves, that does not count as engaged. A low engagement rate might mean your greeting message is not compelling or users are opening the agent by accident.

Resolution rate is your primary success metric. It measures the percentage of sessions that resolved without needing to escalate to a human agent. This is the number your stakeholders care about most. A resolution rate above 60 to 70 percent is solid for most government scenarios. Below 50 percent means your agent needs significant improvement.

Escalation rate tracks how often the agent hands off to a human. Some escalation is expected and healthy—you want the agent to escalate complex issues rather than giving bad answers. But a rising escalation trend signals that the agent is hitting its limits more frequently.

Abandonment rate measures users who leave mid-conversation without resolving their issue or escalating. High abandonment points to confusion or frustration—the user gave up. This is your most actionable metric because it highlights where the experience breaks down.

Average session duration tells you how long conversations take. Unusually long sessions may indicate the agent is going in circles or asking for too much information. Very short sessions with low resolution may indicate the agent is not understanding users at all.

Set benchmarks for your agent. Track these metrics weekly and look for trends rather than fixating on any single data point. A bad day is noise. A bad week is a signal.

Identifying conversation issues

The metrics tell you something is wrong. The Sessions view tells you exactly what.

Navigate to the Sessions section to drill into individual conversation transcripts. You can filter sessions by outcome—resolved, escalated, or abandoned—to focus on the problems.

Look at abandoned and escalated sessions first. Read through the conversation transcripts and look for patterns.

The most common pattern is users rephrasing the same question multiple times. If a user asks a question, the agent does not understand, and the user tries again with different wording two or three times, that topic needs better trigger phrases or a broader set of training examples.

Watch for conversations that hit the fallback topic repeatedly. The fallback topic fires when no other topic matches. If the same type of question triggers fallback consistently, you need a new topic to handle it.

Another pattern is sessions where the agent provides an answer but the user immediately asks the same question again. This usually means the agent gave a wrong or incomplete answer. The user is not satisfied and is trying again.

Topic-level analytics break this down further. You can see which topics are triggered most often, which have the highest resolution rate, and which have the highest abandonment rate. This tells you exactly where to focus your effort.

Use this data to prioritize improvements. Fix the topics that users hit most often and that fail most frequently. A topic with high volume and high abandonment is your highest-priority fix. A topic with low volume and moderate abandonment can wait.

When reviewing session transcripts, be mindful of data sensitivity. Conversation logs may contain PII or sensitive government information. Follow your agency’s data handling policies when sharing analytics findings with your team. Do not paste transcript excerpts into emails or chat without confirming the content is appropriate to share.

Using analytics to improve your agent

Analytics are only valuable if you act on them. Here is the improvement loop.

Step one: review your analytics dashboard. Look at the trends in resolution, abandonment, and escalation.

Step two: identify the biggest issues. Drill into sessions, find the patterns, and pinpoint the topics that need work.

Step three: update your agent. Add new topics for common questions that currently hit fallback. Improve trigger phrases for topics that users struggle to activate. Simplify conversation flows for topics with high abandonment. Expand your knowledge sources for topics where the agent gives incomplete answers.

Step four: republish your agent.

Step five: measure again. Compare the next week’s metrics to the previous week. Did resolution go up? Did abandonment on that problem topic go down?

This loop never really ends. Even a well-performing agent needs regular attention as user needs evolve, policies change, and new questions emerge. Set a regular cadence for analytics review—weekly for the first month after launch, then biweekly as the agent matures and stabilizes.

Track your improvements over time. Keep a simple log of what you changed and when. This gives you a clear record of cause and effect, and it helps you report progress to stakeholders.

Close: Data-driven agents

Let us recap. The analytics dashboard gives you visibility into your agent’s performance. Key metrics—resolution rate, abandonment rate, escalation rate—tell you whether users are getting help. Session-level transcripts show you exactly where things break down. And the improvement loop—review, identify, update, republish, measure—turns data into a better agent.

Publishing an agent is not the finish line—it is the starting line. Analytics tell you what to improve next, and that continuous improvement is what separates a good agent from a great one.

Next up, we will cover governance controls and environment management for managing agents at scale in your organization.

Sources & References

• Analytics overview — Overview of the analytics dashboard and available metrics in Copilot Studio
• Session analytics — Detailed guide for reviewing session-level analytics and conversation transcripts
• Topic details analytics — Topic-level analytics for identifying improvement areas
• Copilot Studio documentation — Main documentation hub for Copilot Studio