Concepts and Terminology

This topic introduces the key terms you must know before working with the Druid AI Platform.

Agentic AI

Agentic AI is a framework to building AI systems that use iterative, multi-step reasoning and actions to achieve a goal, rather than a single prompt-and-response interaction.

AI Agent

An AI Agent is a software system that interacts with users or operates autonomously to perform reasoning, retrieve knowledge, and execute actions within a defined scope. AI Agents can be built using either deterministic, NLP-based flows or the agentic AI framework powered by large language models. All AI Agents remain governed by enterprise rules, integrations, and oversight.

AI Agents are categorized into two types based on their operational mode:

Conversational AI Agents

A Conversational AI Agent combines conversational interface capabilities with actionable tools. Key characteristics include:

Engages in interactive dialogue with users
Provides conversational interface coupled with tools and API access
Executes multi-step workflows (such as creating tickets, resetting passwords, or updating CRM systems)
Can operate using deterministic, NLP-based flows (traditional rule-based approach) or agentic AI reasoning
Handles complex resolution tasks with clarifying questions
Incorporates guardrails, approvals, and policy enforcement

Conversational AI Agents are ideal for service desks, customer support, and employee self-service scenarios.

Autonomous AI Agents

An Autonomous AI Agent operates as an autonomous digital worker or employee that functions independently. Key characteristics include:

Plans tasks and calls tools/APIs autonomously
Operates in the background without requiring user conversation
May be event-driven (triggering actions when specific conditions occur)
May involve multi-AI Agent collaboration (such as planner and executor roles)

Autonomous AI Agents are ideal for automation, operations, workflows, and productivity enhancement.

Artificial intelligence (AI)

Artificial intelligence (AI) is the intelligence demonstrated by machines. It includes concepts like natural language understanding (NLU) and machine learning.

Channel

A channel is the environment that hosts the AI Agent and enables two-way conversation.

You can build and deploy DRUID AI Agents across various channels. For employee-facing AI Agents, consider deploying them on Intranet Web Pages, Microsoft Teams, or Slack. For customer-facing AI Agents, you might deploy them on your public website, Facebook, WhatsApp, Skype, or custom Mobile Apps.

Bot - legacy term

NOTE: The terms "bot", "chatbot" and "virtual assistant" are legacy terminology. In current Druid nomenclature, these terms correspond to AI Agents. Although the help documentation and platform UI may still reference the older terminology in places, you should understand these as AI Agents as described above. This shift in terminology reflects the broader capabilities of the Druid AI Platform to support multiple types of AI Agents based on their operational mode.

Context

Similar to natural language context, the context controls the flow of a conversation. During a conversation flow, the AI Agent saves information in its memory, which is cached in the form of entities and variables.

Druid Conductor

A multi-AI Agent orchestrator that manages the conversation and delegates tasks to specialized Agents as needed.

Acts as the main AI Agent for all user interactions.
Routes requests to specialized AI Agents when needed.
Preserves conversation history across Agent handoffs to ensure a seamless experience.
Enables complex, integrated AI Agents within a single session.

Flow

A Flow configures the dialog between the end-user and the AI Agent. In the Druid AI Platform, a Flow is equivalent to an Intent plus a Dialog.

A Flow uses a tree-like structure where you configure logical decisions to provide different paths based on context, actions, or user input.

Intent

An Intent represents the purpose of a user’s input (an utterance). An Intent categorizes the user’s goal for one conversation turn.

You define many Intents and bundle them together with Dialogs into Flows. You train an AI Agent using training phrases, which are examples of what the user might input. When the end user writes or says something, the DRUID AI Agent matches the user's expression to the best Intent.

For each Intent, you can define actions that trigger specific behaviors in your system. When the AI Agent matches the Intent, it provides the action to your system.

Dialog

A Dialog defines the AI Agent's responses to defined Intents. In the Druid AI Platform, you configure the Dialog as part of the Flow.

Entities

Entities are variables that complement or modify the Intent. An Entity is a snippet of information the AI Agent can understand to serve the correct answer.

Entities are typed memory slots that keep the conversation context. In the Druid AI Platform, entities are structured objects with attributes (or fields) and relations between them. The collection of entities within the AI Agent creates the AI Agent Informational Model.

Example: The Account entity represents a person or organization that buys goods or services. It includes fields like Name, First Name, Last Name, Email, Orders (collection of Orders), Invoices (collection of Invoices), and Payments (collection of Payments).

Utterance

An Utterance is whatever the user says or inputs. The DRUID AI Agent extracts the Intent and Entities essential to carrying on a conversation from the Utterance.

Druid local variable

Druid local variables are defined in Flows and persist throughout the conversation during the Flow execution.

Integration context variable

Integration context variables persist during the connector task execution and are used for custom authentication. Druid keeps them in memory until using them in the authorization call to the business API.

Machine learning (ML)

Machine learning (ML) is the field of AI that focuses on making machines learn. In the context of AI Agents, ML improves the AI Agent's ability to answer complex user queries over time.

Natural language processing (NLP)

Natural language processing (NLP) is a field of AI that encompasses everything related to a machine understanding human input. To do this, an NLP engine uses tools such as natural language understanding, sentiment analysis, and tokenization.

Natural language understanding (NLU)

Natural language understanding (NLU) is the process that examines an utterance and extracts its entities and intent. It allows a machine to understand what a user is saying.