Optimizing an agent doesn't end at launch. Copilot Studio provides rich analytics that help you understand how users interact with your agent, where conversations succeed or break down, and how well the agent uses its tools and knowledge. This article provides a structured checklist and best practices to help you continuously evaluate and improve your agent.
Validate your improvement and analytics readiness
Use these questions during regular reviews, such as sprint ceremonies, monthly optimizations, or pre-release readiness.
Themes and user intent patterns
| Done? |
Task |
| ✓ |
Are you reviewing themes to identify clusters of user questions and emerging intents? |
| ✓ |
Are you adding frequently occurring themes to your backlog for future improvements? |
Conversation outcomes
| Done? |
Task |
| ✓ |
Are you analyzing resolved, escalated, abandoned, and unengaged conversations to find improvement areas? |
| ✓ |
Are you ensuring conversations end with the End of Conversation topic so outcomes are captured correctly? |
| ✓ |
Are you investigating spikes in abandoned sessions to identify unclear responses or missing logic? |
| ✓ |
Are you validating that escalation paths trigger only when appropriate? |
Generated answer rate and quality
| Done? |
Task |
| ✓ |
Do you review the generated answer rate to identify gaps in knowledge or missing coverage? |
| ✓ |
Do you check answer quality metrics such as completeness, groundedness, and relevance? |
| ✓ |
Do you investigate poor‑quality answers and address the reasons flagged in analytics? |
| Done? |
Task |
| ✓ |
Do you monitor how often tools and actions are invoked and whether they succeed or fail? |
| ✓ |
Do you identify underused or error‑prone tools and determine whether to optimize or remove them? |
| ✓ |
Do you validate that tools used in generative orchestration perform reliably? |
| Done? |
Task |
| ✓ |
Do you review the usage and error rates of all knowledge sources? |
| ✓ |
Do you prioritize updates for knowledge sources with high error rates or inconsistent results? |
| ✓ |
Do you verify that the correct knowledge sources support the scenarios they're intended for? |
Satisfaction and user feedback
| Done? |
Task |
| ✓ |
Are you collecting user sentiment through thumbs‑up/down and CSAT surveys? |
| ✓ |
Are you analyzing feedback trends to detect unclear responses or weak conversation flows? |
| ✓ |
Are you adding low‑satisfaction interaction patterns to your backlog for redesign? |
Best practice callouts
- Treat analytics as an iterative improvement loop: Use analytics to drive incremental changes. Use themes, incomplete answers, and failure patterns to inform sprint planning and prioritize backlog items.
- Focus on quality of outcomes, not just volume: A healthy system maximizes resolved conversations and minimizes escalations and abandonment. Use outcome ratios as a leading indicator of clarity and effectiveness.
- Strengthen knowledge sources proactively: High error rate or low-quality answers often point to unclear, outdated, or mismatched knowledge sources. Update and restructure these sources frequently for better grounding.
- Optimize tools for stability and success: Unreliable tool calls degrade trust. Track success rates and refactor actions that frequently fail or return inconsistent data.
- Use themes to identify new opportunities: Themes highlight emerging intents. Use them to inform new topics, knowledge sources, or integration needs.
- Ensure conversations end cleanly: Always use the End of Conversation topic to capture resolution and CSAT. Without this topic, analytics become incomplete and misleading.
- Separate evaluation of autonomous and user‑initiated agents: Autonomous agents rely heavily on triggers and tool chains. Review run outcomes and triggers separately from user‑initiated flows.
- Track sentiment over time: Isolated feedback is useful, but multi‑week sentiment trends reveal systemic issues. Investigate persistent dips early.