# Glossary

Plain definitions of product adoption, onboarding, analytics and AI agent terms.

- [A2A protocol](/glossary/a2a-protocol.md): The A2A protocol (Agent2Agent) is an open standard that lets AI agents built by different teams or vendors discover each other, hand off tasks and exchange results, without exposing their internal logic, memory or tools.
- [Activation rate](/glossary/activation-rate.md): Activation rate is the percentage of new users or accounts who complete a defined activation event, the first action that shows they got real value from the product. It measures how well onboarding turns sign-ups into people who are likely to stay.
- [Agentic AI](/glossary/agentic-ai.md): Agentic AI is the class of AI systems that pursue a goal with some independence. They plan steps, use tools and software to take actions, check the results and adjust, while people set the goal and the limits.
- [Agentic interface](/glossary/agentic-interface.md): An agentic interface is a way of using software in which the user states a goal and an AI agent carries it out inside the live product, through the product's own actions and within the user's permissions. The screens stay, and the agent becomes a second way to get work done.
- [Agentic UI](/glossary/agentic-ui.md): Agentic UI is user interface that an AI agent renders and acts through while it carries out a task, such as a card to confirm, a form to complete or a picker to choose from, shown inside the conversation. It lets the user see, adjust and approve what the agent is about to do.
- [Agentic UX](/glossary/agentic-ux.md): Agentic UX is the practice of designing products in which an AI agent carries out tasks on the user's behalf. It covers how the agent understands what the user wants, when it asks for confirmation, what it is allowed to do, how it shows its work and how the user undoes it.
- [Agentic workflow](/glossary/agentic-workflow.md): An agentic workflow is a multi-step process in which an AI agent plans the steps, calls tools to carry them out, checks each result and decides what to do next, instead of running every case through the same fixed script.
- [Aha moment](/glossary/aha-moment.md): An aha moment is the point where a new user first understands, from their own experience, why a product is valuable to them. Product teams look for the action behind that moment in their data and work to get more new users to it, sooner.
- [AI agent](/glossary/ai-agent.md): AI agents are software that use a language model to understand a goal, decide the steps and act through tools or APIs to complete them, checking results along the way. An AI agent does the task rather than only answering questions about it.
- [AI copilot](/glossary/ai-copilot.md): An AI copilot is an assistant built into a piece of software that works alongside the user. It suggests, drafts, summarizes and answers questions, while the user reviews the output and takes the actions themselves.
- [AI observability](/glossary/ai-observability.md): AI observability is the practice of recording and analyzing what an AI system does once it is live, including each step it took, its cost, latency, output quality and failures, so a team can explain its behavior, catch problems early and see where it falls short.
- [AI-First](/glossary/ai-first.md): AI-First describes a company or team that treats AI as the default way work gets done, designing processes, roles and decisions around it before adding manual steps or headcount. It is an operating model, while AI-Native describes how a product itself is built and used.
- [AI-First Growth](/glossary/ai-first-growth.md): AI-First Growth is a growth motion in which an in-product AI agent does the setup and work that self-serve products used to leave to the user, so that more new users can reach a real outcome in their first session and self-serve can work for complex products.
- [AI-Native software](/glossary/ai-native.md): AI-Native software is software built with AI at the core of how it works, rather than added on as a feature. Users can state the outcome they want in their own words, and the product understands the request and carries out the steps, instead of making them learn menus and screens first.
- [Automated onboarding](/glossary/automated-onboarding.md): Automated onboarding is onboarding in which software, not people, runs the repeatable steps, such as welcome emails, account provisioning, setup reminders, task assignment and handoffs, triggered by what each new user or account has or hasn't done yet.
- [Churn rate](/glossary/churn-rate.md): Churn rate is the percentage of customers, or of recurring revenue, that a business loses over a given period, such as a month or a year. It is the flip side of retention and one of the main measures of whether a subscription business is keeping what it wins.
- [Cohort analysis](/glossary/cohort-analysis.md): Cohort analysis is a way of studying behavior by grouping users who share a starting point or an action, such as signing up in the same month, and tracking each group over time. It shows whether newer users behave better or worse than older ones.
- [Conversational UI](/glossary/conversational-ui.md): A conversational UI is an interface where people use software through a back-and-forth exchange, typed in a chat or spoken aloud, rather than by clicking through screens. It keeps the context of the exchange, so each turn builds on the one before.
- [Customer churn](/glossary/customer-churn.md): Customer churn is when customers stop doing business with a company, by cancelling a subscription or not renewing a contract. In SaaS it usually means a paying account that leaves, and it is the main counterweight to growth from new customers.
- [Customer health score](/glossary/customer-health-score.md): A customer health score is a single rating, often shown as green, yellow or red, that combines product usage, support, relationship and billing signals to estimate how likely an account is to renew, expand or churn. Customer success teams use it to decide which accounts need attention first.
- [Customer onboarding](/glossary/customer-onboarding.md): Customer onboarding is the process of taking a new customer account from purchase or sign-up to the first result it bought the product for. In business software it covers setup, data import, integrations and getting the right people using the product.
- [Customer retention rate](/glossary/customer-retention-rate.md): Customer retention rate is the percentage of customers a business keeps over a given period, counting only the customers it had at the start. It leaves out new customers won during the period, so it shows how well the business holds on to the ones it already has.
- [DAU/MAU ratio](/glossary/dau-mau-ratio.md): The DAU/MAU ratio is a product's average daily active users divided by its monthly active users. It shows what share of the people who use a product in a month come back on a typical day, which makes it a common measure of habit and stickiness.
- [Digital adoption](/glossary/digital-adoption.md): Digital adoption is the degree to which people use the software they have been given well enough to get the outcome it was bought for. It covers employees adopting internal tools and customers adopting a product, and it is measured by real work done in the tool, not by licenses.
- [Feature adoption](/glossary/feature-adoption.md): Feature adoption is the extent to which users discover, try and keep using a specific feature of a product. It is tracked per feature, as the share of eligible users who use it repeatedly, and shows which parts of the product deliver value and which go unnoticed.
- [Function calling](/glossary/function-calling.md): Function calling is how a large language model asks an application to run a specific function. The model returns the function's name and arguments as structured data, the application executes it, and the result goes back to the model to shape its next step.
- [Funnel analysis](/glossary/funnel-analysis.md): Funnel analysis is a method of measuring how many users complete each step in a sequence that leads to a goal, such as sign-up, setup or purchase, and where they drop off. It shows which step loses the most people, so teams know where to focus.
- [Generative experience](/glossary/generative-experience.md): A generative experience is an in-product interface where an AI agent works out what a user is trying to do and does it with them in the live product, instead of showing a fixed, pre-authored sequence of steps. The term describes an agent-led alternative to the product tour.
- [Guided onboarding](/glossary/guided-onboarding.md): Guided onboarding is an approach to onboarding in which the product leads new users through setup and their first tasks step by step, using prompts, checklists, product tours or a person, instead of leaving them to find their own way.
- [Human in the loop](/glossary/human-in-the-loop.md): Human in the loop (HITL) is a design approach in which a person reviews, approves or corrects an automated system's work at defined points, so that decisions and actions with real consequences pass through human judgment before they take effect.
- [In-app guidance](/glossary/in-app-guidance.md): In-app guidance is help delivered inside a software product, at the moment and place a user needs it, rather than in separate documentation or courses. It includes tooltips, product tours, walkthroughs, checklists, banners and contextual help panels.
- [In-app messaging](/glossary/in-app-messaging.md): In-app messaging is communication shown inside a web or mobile product while someone is using it, such as banners, modals, slideouts and small nudges. It reaches active users in the context of what they are doing, unlike email or push notifications, which reach them outside the product.
- [In-app support](/glossary/in-app-support.md): In-app support is help that users get inside the product they are using, such as a help panel with search, in-app chat with a person or an AI agent, or contextual help tied to the current screen. It saves users from leaving the app to search a help center or email support.
- [In-app survey](/glossary/in-app-survey.md): In-app surveys are short questionnaires shown to users inside a product while they use it, such as an NPS question, a CSAT rating or a single question about a feature. Because they appear in context, they capture feedback close to the moment it describes.
- [LLM agent](/glossary/llm-agent.md): An LLM agent is a system that uses a large language model to decide what to do next toward a goal. It plans, calls tools such as APIs or search, reads the results and repeats until the task is done or it needs a person.
- [LLM evals](/glossary/llm-evals.md): LLM evals are repeatable tests that score the output of a large language model or AI agent against what a good answer or action looks like. Teams use them to measure quality, compare changes and catch regressions before users see them.
- [Model Context Protocol (MCP)](/glossary/model-context-protocol.md): The Model Context Protocol (MCP) is an open standard for connecting AI applications to external tools and data. An application's MCP client connects to MCP servers, which expose tools to call, resources to read and prompts to reuse through one common interface.
- [Natural language interface](/glossary/natural-language-interface.md): A natural language interface is a way to use software by typing or saying what you want in everyday words, instead of finding the right menu, form or command. The system works out what you mean and then answers or carries out the request.
- [Net revenue retention (NRR)](/glossary/net-revenue-retention.md): Net revenue retention (NRR) is the share of recurring revenue a company keeps from an existing group of customers over a period, after adding expansion and subtracting downgrades and cancellations. It shows whether the current customer base grows or shrinks on its own, without any new sales.
- [North Star metric](/glossary/north-star-metric.md): A North Star metric is the single measure a product team uses to track whether customers are getting the core value the product promises. It counts that value as customers experience it, predicts long-term revenue, and is something every team can move.
- [Onboarding checklist](/glossary/onboarding-checklist.md): An onboarding checklist is a short list of setup tasks shown to new users or customers, usually inside the product, that tracks what they've done and what's left before they get value from it.
- [Onboarding flow](/glossary/onboarding-flow.md): An onboarding flow is the sequence of screens, steps and prompts a new user goes through between signing up and completing the first meaningful task in a product, such as creating a first project or connecting a data source.
- [Onboarding metrics](/glossary/onboarding-metrics.md): Onboarding metrics, also called customer onboarding metrics, are the measures that show whether new users and accounts get from sign-up to first value: how long it takes, how many get there, where they stop and whether they stay.
- [Onboarding survey](/glossary/onboarding-survey.md): An onboarding survey is a short set of questions shown to new users at or just after sign-up, asking about their role, goal and team, so the product can route each person to the setup and first steps that fit them.
- [Pirate metrics (AARRR)](/glossary/pirate-metrics.md): Pirate metrics, or AARRR, are a framework that splits the customer lifecycle into five stages: acquisition, activation, retention, referral and revenue. Each stage gets its own metric, so a team can see where people drop off and which stage to fix first.
- [Product adoption](/glossary/product-adoption.md): Product adoption is the process by which users start using a product, reach its core value and make it a regular part of how they work. It is judged by whether people keep doing the job the product exists for, not by how many sign up.
- [Product adoption curve](/glossary/product-adoption-curve.md): The product adoption curve is a model of how a new product spreads through a market over time, from innovators and early adopters to the early majority, late majority and laggards. It comes from Everett Rogers' diffusion of innovations theory and is drawn as a bell curve.
- [Product engagement](/glossary/product-engagement.md): Product engagement is how often, how widely and how deeply people use a product over time, from how frequently they return to which features they rely on. It is the behavioral sign that users keep getting value, and an early signal of retention and expansion.
- [Product qualified lead (PQL)](/glossary/product-qualified-lead.md): A product qualified lead (PQL) is a user or account that has shown buying intent through how they use the product, such as reaching a key outcome, hitting a plan limit or inviting teammates. It is scored on product behavior rather than on marketing engagement.
- [Product stickiness](/glossary/stickiness.md): Product stickiness is how consistently users come back to a product on their own because it has become part of how they work. Teams measure it by how often active users return within a period, and treat it as an early signal of retention.
- [Product tour](/glossary/product-tour.md): A product tour is a guided sequence of in-product messages, usually modals, tooltips and hotspots, that shows a new user around a product's main screens and features. Product tours typically run on first login to orient users before they start their own work.
- [Product walkthrough](/glossary/product-walkthrough.md): A product walkthrough is step-by-step guidance that leads a user through completing one specific task inside a product, such as creating a first report or connecting an integration. Each step waits for the user to act before showing the next one.
- [Product-led growth (PLG)](/glossary/product-led-growth.md): Product-led growth (PLG) is a go-to-market strategy in which the product itself drives acquisition, conversion and expansion. People sign up, try the product and reach value on their own, and sales joins later, usually for larger accounts.
- [Product-led onboarding](/glossary/product-led-onboarding.md): Product-led onboarding is an approach in which the product itself, rather than a salesperson or onboarding specialist, takes a new user from sign-up to first value, through in-product setup, sensible defaults, guidance and prompts.
- [Retention curve](/glossary/retention-curve.md): A retention curve is a chart of the share of a group of users who are still active at each point after they start, such as day one, week four or month six. Its shape shows whether a product keeps the people it attracts.
- [Self-serve](/glossary/self-serve.md): Self-serve is a way of selling and onboarding software in which customers sign up, set up, start using and often pay for a product on their own, without a sales call or a person guiding them through it.
- [Session replay](/glossary/session-replay.md): Session replay is a reconstruction of a user's visit to a website or app, rebuilt from recorded page changes, clicks, scrolls and inputs, so a team can watch what the user saw and did. Teams use it to debug issues and find where people get stuck.
- [Time to value](/glossary/time-to-value.md): Time to value (TTV) is the time a new user or account takes to get the first meaningful result from a product after signing up. Teams track it because the longer it takes, the more new users leave before they see why they came.
- [Tooltips](/glossary/tooltips.md): Tooltips are small text labels that appear when a user hovers over, focuses on or taps an interface element, explaining what it is or what it does. In user onboarding, tooltips also act as pop-up hints that point at a feature to draw a new user's attention to it.
- [User engagement metrics](/glossary/user-engagement-metrics.md): User engagement metrics are the measures a product team uses to track how often, how deeply and how consistently people use a product, such as active users, stickiness, session depth, feature adoption and retention. Read together, they show whether users keep getting value.
- [User intent](/glossary/user-intent.md): User intent is the goal a person is trying to reach when they click, search or ask for something. In product analytics, user intent means what users meant to accomplish, which clicks, page views and events only partly reveal.
- [User retention](/glossary/user-retention.md): User retention is the extent to which the people who start using a product keep coming back to it over time and keep doing the action it exists for. It is measured on individual users, not paying accounts, and shows whether a product has become part of their routine.
- [Voice AI agent](/glossary/voice-ai-agent.md): A voice AI agent is software that listens to spoken requests, works out what the person wants and responds or takes action, usually by speaking back in real time. It combines speech recognition, a language model that decides what to do, and speech synthesis for the reply.