Engineers know if your agent is running. But do you know if it’s adding value?
LangFuse, Datadog, Arize, and Braintrust tell you why your AI responded that way. Pendo Agent Analytics tells Product and AI adoption teams what users asked, how AI responded, and whether interactions drive adoption or create risk.
The Market reality for
AI agents
AI agents have moved from pilot phase to live in user workflows. The executive question has shifted from "Can we build an agent?" to "Is it delivering measurable business value?" Developer tools were built for the first question. Pendo was built for the second.
A critical visibility gap: Unlike traditional software, AI agents are non-deterministic — the same input can produce different outputs. Existing observability tools tell you if the system is running. They can't tell you if users are succeeding.
Take Agent Analytics for a spin.
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Pendo Agent Analytics is the first and only observability platform built for Product and AI adoption teams.
Developer tools like LangFuse, Arize, and Datadog are exceptional at answering engineering questions: did the model hallucinate? Did the API time out? How many tokens did we use?
But those tools can't answer the questions Product and Business teams are being held accountable for.
Developer AI Observability
LangFuse / LangSmith, Arize, Datadog, Braintrust
Why did the model respond that way?
What was the latency and token cost?
Did the prompt logic cause a hallucination?
Product AI Observability
Pendo Agent Analytics
Who is using the agent, for what, and how often?
Where are users experiencing problems?
Is the agent-led workflow outperforming the old way?
Three critical insights you can’t get anywhere else.
Quantify AI’s impact
Connect agent usage to retention, task completion, churn, and support tickets.
Identify emerging issues, at scale
Spot issues and user rage within your agent. Then, get suggestions on how to fix it.
Maximize adoption
See who uses your agent, how often, and for what, to prioritize needs and scale what works.
Comparison Guide
How Pendo Agent Analytics compares to developer observability tools
Dev tools tell you if the infrastructure is working. Pendo tells you if the agent is delivering value to users and the business. You need both.
Pendo
Agent Analytics
LangFuse /
Langsmith
Datadog /
Arize
Braintrust
WHO IT'S BUILT FOR
Primary User
Product Managers, CPO, IT or AI adoption teams
AI Engineers,
Data Scientists
SRE, ML Engineers
AI Engineers,
Prompt Engineers
Non-technical user access
OBSERVABILITY FOCUS
User adoption & engagement metrics
Aggregate insights across all user conversations
Emergent use case discovery
Automated agent issue detection within real user instances
Rage Prompt / user frustration detection
LLM trace / span analysis
Coming soon
Token cost & latency monitoring
Partial
Prompt eval / model accuracy testing
BUSINESS IMPACT
Connect agent interactions to downstream user outcomes and product metrics
Agentic vs. traditional workflow comparison
Path and Funnel analysis of user journey across SaaS + Agent experiences
ROI metrics for executive stakeholders
PLATFORM
Session Replay linked to user sessions
In-app guide activation from agent insights
Unified behavioral + conversational analytics
Most organizations need both developer tools and Pendo. They answer different questions for different teams.
“AgentAnalyticsopensupanentirelynewlevelofinsightintocustomerneeds.Whatthey'restrugglingwith,whatthey'reaskingabout,withouthavingtoaskthemdirectly.Thisistheholygrailofvoiceofthecustomer.”
Christopher Penney
Product @ OSAIC
Measure and
scale AI agents.
Agents are changing how you use software. Understand how yours work, so you can improve adoption and get the most from your AI investments.
Watch this 2-minute demo to see Agent Analytics in action.
You're closer to the answer than you think.
See how you can tackle big AI agent challenges, improve adoption, and measure ROI.
Frequently asked questions
Pendo Agent Analytics is a product observability layer built for the teams who own AI agent adoption, outcomes, and ROI. It shows you who is using your agent, how often, and for what, and surfaces issues in real user sessions before they become churn. Further, it gives you the metrics to prove business value to leadership. It sits alongside your developer observability tools, not in place of them.
Yes. Pendo includes 500 prompts captured/month free to start using Agent Analytics. You can even tag your agent in Pendo Free and see real user data before committing to a paid plan.
Yes. Agent Analytics is part of the Pendo platform, so you need a Pendo account to use it. The good news is Pendo Free is genuinely free to start, with no barrier to getting set up.
Installation follows the same process as core Pendo: a JavaScript snippet added to your application. It works across single-page apps, multi-page apps, and iframes, and can be deployed via Google Tag Manager, Twilio Segment, or the npm package if you’d prefer not to modify code directly. Most teams are instrumented in under a day.
Agent Analytics can capture data from any AI agent where you have access to either the browser context (through the Pendo Web SDK or browser extension) or the agent's backend code (through the server-side Conversations API). This includes agents that run in your own web applications (using the Pendo Web SDK), third-party web applications where the Pendo browser extension is deployed for employees (prompts-only), and mobile apps, backend services, or custom integrations where your development team can call the server-side Conversations API (for example, a Slack agent that routes conversations through your own backend).
Core Pendo analytics tracks clicks, page views, and feature usage — traditional product interactions. Agent Analytics extends that to conversational interactions: prompts, agent responses, session outcomes, and frustration signals. The two work together, which is what enables unique capabilities like comparing agentic vs. traditional workflows in the same product view.
Most teams that use Pendo Agent Analytics already have a developer observability tool in place. They’re not replacements for each other. Your engineering team uses LangFuse or Datadog to monitor model performance and debug issues. Pendo gives your product and business teams the layer they’ve been missing: who’s using the agent, whether it’s working for users, and how to prove ROI.
