Beyond Chatbots: Building More Reliable and Context-Aware AI Conversational Agents

Conversational artificial intelligence has changed the way people interact with digital systems. Users can now ask questions in everyday language, receive explanations, obtain recommendations, and complete tasks without learning complicated software interfaces. However, a conversation that sounds natural is not always accurate or dependable. An AI assistant may misunderstand a question, lose track of earlier information, produce an unsupported answer, or use an external tool in an inappropriate way. These issues have become increasingly important as conversational systems move beyond simple question answering towards more autonomous tasks.This paper examines recent developments in AI-based conversational agents through a critical review of selected research published between 2024 and 2026. The 2022 scoping review by Kusal et al. provides the foundation for understanding earlier conversational technologies, including natural-language processing, deep learning, context management, and emotion-aware dialogue. More recent studies extend this discussion to large language models, autonomous agents, retrieval-augmented generation, hallucination mitigation, and agent evaluation.The review suggests that improving the language model alone is not sufficient to address the practical challenges of conversational AI. Reliability also depends on how information is retrieved, how previous conversation details are managed, how responses are checked, and how risky actions are controlled. To address these concerns, this paper proposes a Trustworthy Context-Grounded Conversational Agent Framework. The framework combines context management, evidence-based response generation, response verification, privacy controls, restricted tool access, and human intervention when necessary. The proposed framework has not yet been experimentally evaluated; therefore, its effectiveness must be established through future implementation and comparative testing