Conversational Agent
An AI agent designed for multi-turn dialogue with users, maintaining context across messages and adapting its behavior based on the conversation flow. Conversational agents combine language understanding with task execution within natural dialogue.
Conversational agents are the most common agent type in customer-facing applications. They maintain conversation state, track user intent across multiple turns, handle topic switches gracefully, and interleave information gathering with task execution. Unlike simple chatbots that match patterns to responses, conversational agents reason about dialogue context and make dynamic decisions about how to proceed.
For growth products, conversational agents serve as the primary interface for onboarding, support, sales, and engagement workflows. The quality of the conversational experience directly impacts conversion and retention metrics. Key design considerations include managing conversation memory efficiently (not all history is relevant at every turn), handling ambiguity through clarifying questions rather than guesses, supporting graceful fallback to human agents, and maintaining brand voice consistency. Build conversation flow analytics to identify where users get frustrated or abandon interactions, then use those insights to improve the agent's dialogue strategies.
Related Terms
Model Context Protocol (MCP)
An open standard that defines how AI models connect to external tools, data sources, and services through a unified interface. MCP enables agents to dynamically discover and invoke capabilities without hardcoded integrations.
Tool Use
The ability of an AI model to invoke external functions, APIs, or services during a conversation to perform actions beyond text generation. Tool use transforms language models from passive responders into active problem solvers.
Function Calling
A model capability where the AI generates structured JSON arguments for predefined functions rather than free-form text. Function calling provides a reliable bridge between natural language understanding and programmatic execution.
Agentic Workflow
A multi-step process where an AI agent autonomously plans, executes, and iterates on tasks using tools, reasoning, and feedback loops. Agentic workflows go beyond single-turn interactions to accomplish complex goals.
ReAct Pattern
An agent architecture that interleaves Reasoning and Acting steps, where the model thinks about what to do next, takes an action, observes the result, and repeats. ReAct combines chain-of-thought reasoning with tool use in a unified loop.
Chain of Thought
A prompting technique that instructs the model to break down complex problems into sequential reasoning steps before producing a final answer. Chain of thought significantly improves accuracy on math, logic, and multi-step tasks.