AI agent vs chatbot: Key differences explained

The blog post by Jim Allen Wallace explores the distinctions between chatbots and AI agents, emphasizing the architectural differences that determine their suitability for various tasks. Chatbots, which can be rule-based or powered by large language models (LLMs), typically handle straightforward, text-based interactions without taking external actions, making them ideal for predictable tasks like customer support. In contrast, AI agents use a reasoning and acting loop, enabling them to perform complex tasks by autonomously interacting with external systems and tools. The evolution from chatbots to agentic systems spans several generations, with developments in LLMs and frameworks like ReAct enhancing their capabilities. The decision to use a chatbot or an AI agent depends on the complexity of the task, the need for tool access, and considerations of cost, latency, and governance. Redis is highlighted as a platform that supports the infrastructure needs of both chatbots and agents, offering features like real-time context, vector search, and semantic caching to optimize performance and reduce complexity in AI applications.