In this research, a task-driven autonomous agent is proposed that leverages OpenAIs GPT-4 language model, Pinecone vector search, and the LangChain framework to perform various tasks within different contexts and constraints. The system is capable of completing tasks, generating new tasks based on completed results, and prioritizing tasks in real-time. The methodology involves processing the task at the front of the task list using GPT-4 and LangChains chain and agent capabilities to generate a result, which is then stored in Pinecone. New tasks are generated based on the completed tasks result using GPT-4 while ensuring that they do not overlap with existing ones. The research demonstrates the potential of AI-powered language models to autonomously perform tasks in diverse domains and discusses future potential improvements such as the integration of a security/safety agent, expanding functionality, generating interim milestones, and incorporating real-time priority updates.
