GraphBit: A Graph-based Agentic Framework for Non-Linear Agent Orchestration
GraphBit introduces a novel graph-based agentic framework for orchestrating non-linear agentic workflows. This approach enhances the flexibility and adaptability of AI agents in complex, dynamic environments.
GraphBit is a new graph-based agentic framework designed for the non-linear orchestration of AI agents. This innovative approach allows for more flexible and adaptable agentic workflows in dynamic environments. The framework was authored by Yeahia Sarker and two other researchers.
This paper, "GraphBit: A Graph-based Agentic Framework for Non-Linear Agent Orchestration," was submitted to arXiv on March 8, 2026. It is available to view as a PDF.
arXivLabs, an experimental projects framework, supports the development and sharing of new arXiv features. Collaborators, including individuals and organizations, adhere to arXiv's values of openness, community, excellence, and user data privacy.
Related articles
The AI world is getting ‘loopy’
AI models are taking a significant leap forward with the adoption of "agentic loops," where AI agents continuously prompt each other to improve code and solve complex problems. This approach, though potentially resource-intensive, promises to unlock new levels of autonomous problem-solving and efficiency in AI applications.
Codex-maxxing for long-running work
Codex is increasingly being used by organizations to support long-running projects that go beyond a single prompt. This whitepaper by Jason Liu offers practical strategies for leveraging Codex as a persistent workspace, managing complex workflows and sustaining progress.
Nobel laureate John Jumper is leaving DeepMind for rival Anthropic
Nobel laureate John Jumper is departing Google DeepMind to join its competitor, Anthropic, after dedicating nearly nine years to DeepMind, where he led the AlphaFold team. Jumper, who shared a Nobel Prize for his work on AlphaFold, expressed gratitude for his time at DeepMind while looking forward to new endeavors.