Inhaltsverzeichnis
What are AI Agent Analytics?
AI Agent Analytics is the practice of measuring, analyzing, and optimizing the performance of AI agents to understand their real-world effectiveness and impact on business outcomes.
This emerging technology focuses on tracking how end-users interact with agents, identifying performance gaps, and improving agent capabilities through data-driven insights.
Why do you need AI Agent Analytics in 2025?
As AI agents become integral to business operations—from customer service chatbots to enterprise workflow automation—organizations need comprehensive visibility into their performance.
Without AI Agent analytics, you have no way to understand if your agents are delivering on their promises of speed, cost savings, and potential revenue gains. Or, if users end up frustrated after one use and ignore your AI agents after that. In addition, enterprises need to understand what end-users are turning to AI agents for, so they can spot potential compliance and regulatory risks.
AI Agent Analytics helps enterprises:
- Measure ROI from AI by tracking conversion rates, task completion, and user satisfaction so you can prioritize improvements and justify continued AI investments.
- Identify weaknesses where agents fail to meet user needs or break down in complex scenarios.
- Optimize performance through continuous monitoring of response accuracy, speed, and user engagement.
- Increase adoption with visibility into which agents are gaining traction, what users are trying to achieve, where drop-offs occur, and how usage varies by role and workflow.
- Ensure compliance with industry standards and regulatory requirements for AI systems with complete visibility into internal and third-party agents.
- Scale intelligently by understanding which agent capabilities deliver the most business value.
Who should use AI Agent Analytics?
AI Agent Analytics delivers measurable value across multiple organizational functions, providing each team with tailored insights to optimize their specific objectives and drive business outcomes.
Produktteams
Product managers need comprehensive visibility into agent performance to build better customer experiences and maximize adoption. Agent Analytics provides the data-driven insights required to iterate intelligently on agent capabilities.
For example, a product manager can use Agent Analytics to understand how often users return to an agent (retention) and how it impacts downstream behavior. Because Pendo Agent Analytics is connected to behavioral data, user feedback, replays, and communication tools, PMs can see what users do before and after engaging with an agent, and nudge them via guides or email to improve engagement and adoption.
IT teams
As an IT leader, you need to ensure AI agents operate securely, compliantly, and deliver ROI. Are your employees using agents as they should be? Are agents opening up regulatory and compliance risks? And most importantly, are your AI agent investments paying off?
IT departments require robust monitoring and governance capabilities to manage enterprise AI deployments effectively. Agent Analytics provides the oversight and control mechanisms necessary for responsible AI implementation.
For instance, an IT department might use Agent Analytics to understand the most common prompts users submit, if they’re uploading confidential company information, and if AI agents are working as they should.
Finanzteams
Finance leaders need concrete metrics to evaluate AI agent performance against business objectives and optimize budget allocation across different agent initiatives. Should you continue investing in AI agents, or should you pivot your investment strategies?
With agent analytics, finance teams can discover that their HR chatbot handles 60% of routine employee queries, saving $200,000 annually in HR staff time. These kinds of findings make it easy to justify continuing—or even expanding—AI investments.
What data does Agent Analytics collect?
Pendo Agent Analytics helps you log and analyze all AI agent usage, including user-submitted prompts, so you can track how conversational AI tools are being used across your organization.
Agent analytics gives you event data across the board—and by top use cases—including:
- Top use cases: What are users coming to your agent to accomplish? Content creation, code development, general learning and education, etc.
- Prompt volume: Is this agent being used? If so, how much?
- Retention rate: When someone uses this agent, how likely are they to return?
- Visitors: How many unique end-users are coming to this agent? Is this trending up or down, over time?
- Suggested prompt rate: Are your recommended genAI prompts relevant and getting selected by your end-users?
- Accounts: How many accounts are coming to your AI agents? Which accounts are they?
Because all of this information is available for agents and for specific use cases, you can fine-tune every aspect of your agent’s performance.
How to optimize AI Agent performance
You create agentic workflows to increase speed and outcomes. But to justify your agent investments, you must compare the old way of completing this task or workflow at the same time as the new way. If agents aren’t actually saving users’ time or helping them increase output, they need to be improved (or removed altogether).
This takes analyzing interaction metrics to:
- Identify successful interactions and encourage other users to adopt these use cases.
- Detect and resolve common issues, like negative user feedback, before they escalate into support tickets.
- Analyze users’ in-app behavior (like common features used, and role types) to tailor more personalized AI responses.
- Improve AI agent training and data inputs based on performance analytics.
- Continuously monitor agent performance metrics like escalation rates, first-contact resolution, and user satisfaction.
Armed with these insights, you can identify the best path forward and act quickly. This is true for most agents, whether you’re building and selling them or buying them for your workforce.
Improving the agents you build and sell
For agents you build and sell, you need to continue driving adoption and justify continued investments. The best way to do this is by:
- Quantifying usage instantly, like the number of prompts created, and by which segments.
- Identifying the top use cases for your agent to understand user needs, and improve messaging.
- Connect agent interactions to downstream behaviors, like task completion or conversion, to confirm whether they improve or hinder user workflows.
Improving the agents you buy and deploy
For the agents you buy and deploy internally, you can make sure you’re eliminating inefficiencies and preventing compliance risks by:
- Understanding AI agent usage and reducing the risks of misuse or ineffective spending.
- Speeding up AI adoption by knowing which use cases are most successful, including by role type.
- Verifying if agent usage is improving employee workflows by connecting interactions to downstream behaviors.
Regularly reviewing insights from Agent Analytics supports ongoing improvements, ensuring AI agents deliver consistent value and measurable ROI.
Why choose Pendo Agent Analytics?
Over 14,000 companies already trust Pendo for its quantitative data, qualitative feedback, and session replays—paired with in-app guides and email communications tools like .
Agent Analytics is Pendo’s newest addition to the platform, with:
- Contextualized, real-time user insights: Pendo is the only platform that links agent performance and prompts to real user behavior and business outcomes, so you can see how agent usage impacts retention, adoption, and engagement in other parts of your product.
- Enterprise-grade security: Trust and transparency are foundational to how Pendo builds, deploys, and scales AI. That’s why no customer data is shared, or feeds into any Pendo-specific models. Our AI is SOC-2, Type II and HIPAA compliant.
- Powerful third-party integrations: Pendo’s data is powerful, especially when combined with your business data or in custom AI models. Push Pendo data into the Business Intelligence systems you care about—like Snowflake—and let them transform your decision-making with Pendo Data Sync.
Common AI Agent Analytics FAQs
How frequently should ROI be reassessed?
Ideally, product and IT leaders should assess agent performance monthly to adapt to changing conditions and agent performance.
Can data be segmented for deeper insights?
Yes, Pendo’s Agent Analytics supports detailed segmentation by customer cohorts, agent types, date ranges, and interaction scenarios.
What distinguishes automated interactions from manual ones?
Automated interactions are AI-driven without human intervention, offering consistent scalability and cost efficiency.
Can Agent Analytics help identify potential improvements in AI agent workflows?
Absolutely. Detailed analytics can highlight bottlenecks and inefficiencies, enabling targeted optimization of agent workflows
Is Pendo capable of tracking a user’s satisfaction with agents?
With Pendo Listen’s feedback management tools, you can understand how users feel about agents directly within Pendo.
