Refers to a system or program that autonomously performs tasks for a user or another system. Agents are more sophisticated than AI assistants.

AI chatbot assistants use conversational AI techniques such as natural language processing (NLP) to understand user questions and automate responses.  These non-agency AI chatbots are ones without available tools, memory, and reasoning. The non-agency chatbots require continuous user input to respond, can produce responses to common prompts that align with user expectations, but perform poorly on questions unique to the user and their data. Since these chatbots do not hold memory, they cannot learn from their mistakes if their responses are unsatisfactory.

By contrast, AI agents can learn to adapt to user expectations over time. The results include more personalized experiences and comprehensive responses. Agents can complete complex tasks by creating subtasks without human intervention and consider different plans. These plans can be self-corrected and updated as needed. AI agents assess their tools and use their available resources to fill in information gaps. Since AI agents often do not have the full knowledge base needed to take on all subtasks within a complex goal, they use their available tools to include external data sets, web searches, APIs, and other agents. After the agent obtains missing information via these means, the agent can update its knowledge base and reassess its plan of action, self-correct, solve complex tasks in various enterprise contexts such as software design, IT automation, code-generation tools, and conversational assistants. The agent uses advanced natural language processing techniques of large language models (LLMs) to comprehend and respond to user inputs step-by-step and determine when to call on external tools.