LLM agents differ significantly from traditional chatbots in their ability to perform complex, multi-step tasks with greater autonomy. While conventional chatbots follow predetermined paths and respond within fixed parameters, LLM agents leverage large language models to independently handle workflows and dynamically manage decision-making. They can execute tasks, access various external tools, and adjust their actions as needed, allowing for more sophisticated interactions[1][2].
Moreover, LLM agents are capable of recognizing when a workflow is complete and proactively correcting actions if necessary. This flexibility enables them to process inputs dynamically and engage in more effective problem-solving than basic chatbots, enhancing their overall functionality[2][1].
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