Dialogit

Dialogit is a term used to describe a framework and schema for developing structured dialogues for conversational agents. It aims to separate dialogue design from natural language understanding and external integrations, providing a declarative way to specify how a conversation progresses, what context is maintained, and how actions are triggered in response to user input.

In typical implementations, dialogit comprises a dialogue model, a context store, and an execution engine. The model encodes states, intents, and transition rules; the context store tracks variables such as user preferences and collected slots; the engine evaluates conditions and dispatches responses, function calls, or API requests.

Dialogit definitions are often authored in a domain-specific language or in common data formats such as YAML or JSON. This allows designers to describe conversational flows as sequences of states with conditions, handlers, and fallback paths, making behavior machine-readable and testable.

Runtime components include adapters for messaging platforms, integration layers, and testing utilities. A dialogue studio or authoring tool may be used to visualize flows, simulate interactions, and verify coverage of edge cases.

Use cases include customer-support chatbots, procedural assistants, and educational tutors. While not a formal standard, dialogit has circulated as a conceptual approach that emphasizes modularity, portability, and the separation of dialogue control from language interpretation.

See also: dialogue management, conversational AI, dialog system, natural language understanding.