Agent Analytics FAQ

As AI agents move from prototype to core product feature, a new measurement problem has emerged: most teams have no reliable way to know whether their agent is actually working. Observability tools tell you what broke. Product analytics tools track clicks and conversions. Evals catch regressions on scenarios you anticipated. But none of them tell you what your users are genuinely trying to accomplish, where they're hitting friction, and whether conversations are reaching successful outcomes at scale. That gap is what Agent Analytics is designed to close. Below, we've answered the questions we hear most often from product and engineering teams who are starting to take agent performance measurement seriously.

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What is the difference between Agent Analytics and observability? Observability tools are built for engineers investigating a specific technical incident: why a tool call failed, where latency spiked, what error was thrown in a particular invocation. The lens is always engineering and the unit of analysis is always a single trace. Agent Analytics operates at the product level, across all your conversations, continuously. It tells you what users are trying to accomplish, whether they're succeeding, and where patterns of failure are accumulating across your entire user base. Both matter. They answer different questions for different teams.

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What is the difference between Agent Analytics and evals? Evals are tests you write in advance for scenarios you've already defined. They catch regressions on known failure modes before you ship. Agent Analytics measures what's actually happening with real users in production, including the failure modes you didn't know to write evals for. The two work well together: Agent Analytics surfaces the patterns that tell you what evals to write next.

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What are intents, corrections, and resolutions in Agent Analytics? These are the three core measurement primitives of Agent Analytics. Intents are what users are actually trying to accomplish in a conversation. Corrections are the moments where users have to rephrase, push back, or clarify because the agent didn't deliver what they needed on the first attempt. Resolutions measure whether the conversation reached a successful outcome. Together they give you the feedback loop that turns a static agent into one that improves over time.

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How is Agent Analytics different from product analytics tools like Amplitude or Mixpanel? Product analytics tools are built around discrete events: clicks, page views, conversions. They work well when user behavior maps to a predictable funnel. Agent conversations are unstructured, variable, and don't map cleanly to predefined event types. A 12-turn conversation where the user changed direction three times produces no natural conversion event. Agent Analytics is built specifically for this data type, inferring intents, corrections, and resolutions from unstructured conversation data rather than relying on manually instrumented events.

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What is a resolution KPI and how do I define one for my agent? A resolution KPI is the single metric that defines what a successful agent interaction looks like for your specific product. It could be a support ticket that didn't get opened, a transaction that completed, a question answered without a human stepping in, or a conversion event fired from your backend. The exact definition depends on your product. The important thing is picking one clear metric and building your measurement practice around it. Everything else in Agent Analytics flows from that definition.

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Can Agent Analytics connect to my existing data warehouse? Yes, and it should. One of the core requirements of a good Agent Analytics platform is the ability to connect conversation-level data to the broader events happening in your product and business. That means supporting ingestion of frontend and backend events, and connecting to your data warehouse so your data team can run deeper analyses that go beyond what fits in a dashboard.

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How is Agent Analytics different from uploading logs to ChatGPT? Uploading logs to ChatGPT or Claude is a workaround, not a workflow. Context windows cap out quickly with high conversation volumes. LLMs don't do aggregate math reliably. The analysis isn't consistent or repeatable week over week. And ad-hoc log analysis is missing all the context from events happening elsewhere in your product. Agent Analytics gives you a consistent, repeatable measurement layer that scales with your conversation volume and connects to your full product data.

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When should I start thinking about Agent Analytics? When agents become a primary feature of your product rather than a side experiment, when your engineering team starts getting pulled into log digs to answer product questions, or when you're shipping changes to your agent without a reliable way to measure whether they helped. A rough rule of thumb: if you're running more than 1,000 agent conversations a month and don't have a clear resolution KPI, it's time.

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Should I build or buy an Agent Analytics solution? For most product teams, buying is the right call. Building even a basic intent detection and resolution tracking pipeline is a real data engineering project, and maintaining it as LLM provider APIs change is ongoing work for multiple people. The teams that get the most value from Agent Analytics are the ones focused on using it to improve their agents, not on maintaining the infrastructure underneath. The exception is if you're building an agent-powered product for other companies and want to surface analytics directly to your customers, though even then most teams want their own global performance view alongside it.

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What is eval drift and why does it matter? Eval drift happens when your test suite falls out of sync with how users are actually using your agent in production. You write evals based on the use cases you anticipated at launch, but real user behavior evolves. Over time, a growing portion of your evals are testing scenarios that no longer reflect production reality, while the failure modes that actually matter go uncovered. Agent Analytics helps address eval drift by continuously surfacing what users are actually doing, giving you the data to keep your eval suite current and focused on what matters.

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How does agent versioning work in Agent Analytics? Agent versioning lets you tag each meaningful change to your agent: a prompt update, a tool modification, an orchestration change, as a distinct version, then compare performance metrics across versions. Instead of guessing whether a change helped, you can see directly how resolution rates, correction frequency, and intent coverage moved between versions. For teams running multiple agents across a product, a good Agent Analytics platform tracks all of them in one place.

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Who should have access to Agent Analytics data in my organization? All of the teams involved in building and improving your agent should have access, not just engineering. Product owns the resolution KPI and tracks intent trends. Engineering uses analytics to diagnose and fix issues at scale. Analysts and data scientists connect agent performance to the data warehouse and build deeper models. Leadership needs a reliable answer to whether the agent investment is paying off. If only one team has visibility into agent performance data, that's as much a coordination problem as a tooling one.

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The teams getting the most value from Agent Analytics share one thing in common: they've stopped treating agent quality as a feeling and started treating it as a metric. That shift from intuition to measurement is what separates agents that quietly plateau from ones that compound in capability over time. If any of the questions above resonated with where your team is right now, we'd love to hear what you're building and what's making measurement hard.