Conversational AI
Conversational AI supports interaction through a sequence of user and system turns, retaining enough context to interpret follow-up requests and complete useful tasks. The practitioner designs responses, clarification, state and handoff behavior so the conversation remains coherent and connected to actual application outcomes.
What it is
A conversational system combines language interpretation, dialogue management and response production. It can use intent classifiers, generative models or a hybrid, with tools providing external data and actions. Context links a turn such as 'change it to Friday' to the correct earlier object, while dialogue policy determines whether to clarify, act or hand off. This is broader than an LLM chat interface and different from speech infrastructure, which adds audio transport and turn-taking concerns. The essential competence is managing a continuing interaction in which users can revise goals, omit details and depart from the expected path.
What the work involves
The practitioner identifies supported tasks and designs conversations around the information needed to complete them. Clarification and confirmation should be proportional to ambiguity and consequences. Session state must track user corrections and tool outcomes. A useful deliverable includes representative dialogues, fallback behavior and a human handoff carrying the relevant context. Evaluation tests multi-turn success, interruptions, topic changes and unsupported requests, checking whether the assistant preserves the user's goal rather than optimizing individual responses in isolation.
Illustrative example
A support assistant helps a user identify a delayed order. The user first gives a date, then corrects the address and asks whether the package can be redirected. The system updates the relevant state, checks shipment rules and explains the available option. When the issue requires a person, it transfers the verified order identifier and conversation summary. Tests inspect that the handoff contains the correction and that no outdated address is used for an action.
Limits and common mistakes
Fluent replies can hide lost context, incorrect assumptions or a failure to complete the task. A long conversation history does not guarantee accurate state tracking. Generic fallback responses can trap users in loops, and excessive clarification makes simple tasks burdensome. Quality is measured over complete interactions, including recovery and handoff. The assistant should state the scope it can handle and recognize unsupported requests instead of maintaining conversational smoothness by inventing facts or capabilities.
Prerequisites
Related skills
- → is subcategory of: AI Agent Design
Sources and further reading
- Dialogflow CX agents
Explains conversational agents as systems translating user text or audio into structured application interactions.
Last updated: 2026-10-10