A structured comparison of AI chatbots and AI agents for developers choosing between the two approaches. Chatbots are reactive, conversation-focused systems built around NLU, dialogue management, and response generation. AI agents extend LLMs with planning, tool usage, memory, and autonomous multi-step execution. The post covers core architectures, real-world use cases, advantages and limitations, implementation frameworks (Dialogflow, Rasa, LangChain, AutoGen, CrewAI), and decision criteria for when to use each. It also addresses hybrid architectures where a chatbot handles the conversational layer while agents execute complex tasks behind the scenes.
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