Conversational Agents

Conversational agents study how AI systems understand and respond to people across multiple turns, from identifying intents and tracking dialogue state to helping with tasks, learning, and support. Common approaches combine language models with retrieval, tools, dialogue planning, reinforcement learning, and memory; voice and multimodal systems also address speech, turn-taking, and visual or sensor input. Evaluation ranges from task-oriented benchmarks such as MultiWOZ and τ-Bench to end-to-end voice tests and studies with users in settings like education, health, and customer service. A central challenge is that fluent, satisfying responses do not guarantee reliable task completion, appropriate intervention, or safe behavior over long interactions, while simulations and automatic judges may not reflect real users well.