ChatGPT Work: persistent AI agent for complex tasks
ChatGPT Work is an OpenAI tool that performs ongoing, complex tasks autonomously, persisting for hours or days. It uses LLMs to plan and execute multi-step workflows, solving the problem of AI agents that stop after a few minutes. Aimed at professionals needing long-running automated assistance.
Visit project ↗Entities
Related
OpenAI launches ChatGPT Work agent with Codex for multi-hour autonomous tasks
OpenAI announced ChatGPT Work, a new agent that can operate across apps and files, stay with complex projects for hours, and produce finished materials like sheets, slides, docs, and web apps. It incorporates Codex technology to move beyond answering questions to autonomous task completion.
Hermes Agent: personal AI with memory, tools, and daily workflow integration
Hermes Agent is a personal AI agent that goes beyond chatbots by incorporating memory, tools, and daily workflow integration. It uses LLMs to remember user context, execute actions via tools, and become a persistent part of the user's routine, solving the problem of AI tools being ephemeral and disconnected from daily tasks.
OpenAI launches ChatGPT Work with confusing cloud-local split
OpenAI released ChatGPT Work, a new tier that runs conversations in the cloud on web and mobile, while the desktop app can also access local files and apps. At launch, cloud Work conversations are not visible in the desktop Work interface, and desktop threads remain local. The rollout aims to offer a more integrated productivity experience but has drawn criticism for its confusing cross-platform behavior.
Local-first coding agent for long autonomous runs
Grinta is a local-first coding agent designed for long autonomous runs. It uses LLMs to autonomously plan and execute coding tasks, solving the problem of needing constant human supervision for extended development sessions.
Article explains how LLMs use tools and iterate to complete tasks
A technical article titled 'The Agent Loop: How AI Learns to Think, Act, and Get Things Done' describes how LLMs use tools, make decisions, learn from results, and iterate until tasks are complete. The piece provides a conceptual overview of agentic AI workflows.