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A Dialogflow
agent
is a virtual agent that handles concurrent conversations with your end-users.
It is a natural language understanding module that understands the nuances of human language.
Dialogflow translates end-user text or audio during a conversation
to structured data that your apps and services can understand.
You design and build a Dialogflow agent to handle the types of conversations required for your system.
A Dialogflow agent is similar to a human call center agent.
You train them both to handle expected conversation scenarios,
and your training does not need to be overly explicit.
Agents also serve as a top-level container for settings and data:
Agent settings
for language options, machine learning settings,
and other settings that control the behavior of your agent.
Intents
to categorize end-user intentions for each conversation turn.
Entities
to identify and extract specific data from end-user expressions.
Knowledge
to parse documents (for example, FAQs) and find automated responses.
Integrations
for applications that run on devices or services
that directly handle end-user interactions for you (for example, Google Assistant).
Fulfillment
to connect your service when using integrations.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-07 UTC."],[[["\u003cp\u003eA Dialogflow agent is a virtual agent that manages multiple conversations with end-users and understands the complexities of natural human language.\u003c/p\u003e\n"],["\u003cp\u003eDialogflow agents translate user text or audio into structured data that applications and services can understand.\u003c/p\u003e\n"],["\u003cp\u003eThese agents are trained to handle various conversation scenarios, much like human call center agents, but with the advantage of not needing overly explicit training.\u003c/p\u003e\n"],["\u003cp\u003eDialogflow agents contain settings and data, including language options, intents, entities, knowledge, integrations, and fulfillment to control agent behavior.\u003c/p\u003e\n"]]],[],null,["# Agents\n\nA Dialogflow\n\n*agent*\n\nis a virtual agent that handles concurrent conversations with your end-users.\nIt is a natural language understanding module that understands the nuances of human language.\nDialogflow translates end-user text or audio during a conversation\nto structured data that your apps and services can understand.\nYou design and build a Dialogflow agent to handle the types of conversations required for your system.\n\n\nA Dialogflow agent is similar to a human call center agent.\nYou train them both to handle expected conversation scenarios,\nand your training does not need to be overly explicit.\n\nAgents also serve as a top-level container for settings and data:\n\n- [Agent settings](/dialogflow/docs/agents-settings) for language options, machine learning settings, and other settings that control the behavior of your agent.\n- [Intents](/dialogflow/docs/intents-overview) to categorize end-user intentions for each conversation turn.\n- [Entities](/dialogflow/docs/entities-overview) to identify and extract specific data from end-user expressions.\n- [Knowledge](/dialogflow/docs/knowledge-connectors) to parse documents (for example, FAQs) and find automated responses.\n- [Integrations](/dialogflow/docs/integrations) for applications that run on devices or services that directly handle end-user interactions for you (for example, Google Assistant).\n- [Fulfillment](/dialogflow/docs/fulfillment-overview) to connect your service when using integrations."]]
