> For the complete documentation index, see [llms.txt](https://docs.helvia.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.helvia.ai/resources/glossary.md). # Glossary This glossary defines the terms you will meet across Helvia.ai Agent Platform and the wider world of conversational AI. Use it as a quick reference when a word is unfamiliar or used in a specific way here. ### Agent An AI worker that understands language, draws on knowledge, and takes actions to hold conversations and automate tasks. In the platform, the agent is what you build, deploy, and monitor, combining reasoning, knowledge, and plugins to complete a job. ### Article A single, self-contained piece of content that an agent retrieves to answer a question. Long documents are usually split into several articles, so the agent can pull only the most relevant part. ### Canvas The visual editor where you build a workflow by connecting nodes and edges. The canvas lets you lay out an agent's logic step by step, without writing code. ### Channel A medium where users reach an agent, such as a website widget, WhatsApp, or a direct API. The same agent can run across several channels at once. ### Chatbot A software application that holds a conversation with users through text or voice. Helvia.ai calls the conversational systems you build agents rather than chatbots, since they do more than answer scripted questions. ### Contact A person who has interacted with an agent, together with the information captured about them. A contact record ties together a user's details and conversation history, so an agent can recognize them over time. ### Conversational AI Technology that lets software understand natural language and respond in a back-and-forth dialogue. It combines language understanding, context tracking, and response generation. ### Deployment A published instance of an agent on a specific channel, such as a website or a messaging app. Each deployment carries its own settings, so one agent can behave differently depending on where it runs. ### Designer Where you build and configure an agent, including its workflows, knowledge, plugins, and deployments. Designer is where most of your build work happens. ### Escalation / Handoff The moment a conversation passes from an agent to a human, usually when a request is too complex or sensitive for automation. A smooth handoff keeps the conversation history, so the person does not have to start over. ### Evaluator An automated judge, powered by an LLM, that reviews a test conversation and decides whether it met your success criteria. It returns a pass or fail result along with the reason. ### Guardrails The rules and limits that keep an agent's responses safe, accurate, and on-topic. Guardrails stop an agent from answering outside its scope or in ways that break policy. ### Integration A stored connection to an external service, holding the credentials and settings an agent needs to reach it. Integrations are set up once and reused across agents. ### Intent The goal behind a user's message, such as booking an appointment or checking an order. Identifying intent is how an agent works out what the user actually wants and how to respond. ### Knowledge Base A collection of content that an agent draws on to ground its answers in your own information. It can be built from uploaded files, written content, or connected external sources. ### LiveChat The platform's built-in application for live conversations between users and your support team, handled inside the platform. When an AI agent hands off a conversation, your team takes it over in LiveChat without the user switching channels or repeating themselves. ### LLM (Large Language Model) The AI model that understands and generates natural language. It powers an agent's ability to interpret messages, reason, and write replies. ### Node A single step in a workflow, such as sending a message, calling an LLM, or branching on a condition. Nodes connect with edges to set the order in which an agent moves through a conversation. ### Observatory Where you monitor and test your agents after they go live. Observatory holds your sessions, analytics, and automated tests in one place. ### Plugin An add-on that extends what an agent can do, often by using an integration to connect to an outside service. Plugins cover capabilities like language detection, testing, and ticketing. ### RAG (Retrieval-Augmented Generation) A technique where an agent first retrieves relevant content, then generates an answer based on it. This keeps responses grounded in real sources instead of the model's memory alone. ### Session A single conversation between a user and an agent, including the full transcript and the data captured during it. Sessions are the record you review to understand what happened in a conversation. ### Signal (insight) A structured insight that AI analysis extracts from a conversation, such as its sentiment, resolution, or urgency. Signals turn raw conversations into data you can measure, compare, and act on at scale. ### Synthetic User A simulated user, driven by AI, that talks to an agent as part of automated testing. It lets you check how an agent handles realistic conversations without needing real people. ### Variables Values an agent captures and reuses during a conversation, such as a user's name or an earlier answer. In the platform, each variable has a scope that sets how long it lasts, from a single question to a returning contact. ### Workflow (Flow) A defined set of steps that determines how an agent behaves in one part of a conversation. An agent is built from many workflows, each handling a specific task or topic. ### Workspace The top-level container that holds a team's agents, users, and shared resources such as integrations and knowledge. Everything you build lives inside a Workspace, and its data stays separate from other Workspaces. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.helvia.ai/resources/glossary.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.