A new system, known as Murakkab, optimizes the design and deployment of multistep workflows that power AI applications.
arXiv:2609.00384v1 Announce Type: new Abstract: Large Language Model (LLM) agents increasingly automate multi-step engineering workflows through tool use, interpretation of intermediate results, and...
The article reviews the emergence of Agentic AI, covering its evolution, theoretical foundations, working principles, and architectural aspects. It surveys recent scholarly contributions across various domains, highlighting real‑world applications, current research findings, and existing challenges. The review also proposes a framework for stakeholder adoption and outlines future research directions to guide researchers and practitioners.
Cloudflare brings OpenAI’s GPT-5. 4 and Codex to Agent Cloud, enabling enterprises to build, deploy, and scale AI agents for real-world tasks with speed and security.
arXiv:2606. 24937v1 Announce Type: cross Abstract: The Hitchhiker's Guide to Agentic AI is a comprehensive practitioner's reference for building autonomous AI systems.
Orchard is an open-source framework for the research community to train and evaluate AI agents across task types. It reduces complexity while supporting strong performance from smaller models by enabling researchers to reuse the same infrastructure.
Stateful Runtime for Agents in Amazon Bedrock brings persistent orchestration, memory, and secure execution to multi-step AI workflows powered by OpenAI.
arXiv:2607. 28629v1 Announce Type: new Abstract: The rapid transition from reactive large language models (LLMs) to persistent, action-capable systems has exposed critical gaps in the architectural understanding of Agentic AI, particularly in separating inference, orchestration, and execution layers for autonomous AI agents.
arXiv:2606. 12835v1 Announce Type: cross Abstract: The rapid emergence of autonomous AI agents is transforming artificial intelligence from isolated model inference into distributed systems of reasoning, communication, and action.
arXiv:2607. 14158v1 Announce Type: new Abstract: This position paper explores how Agentic AI and Model Context Protocol (MCP) can support power-grid studies in a Transmission System Operator (TSO) context.
The hidden cost of asynchronous systems, how tiny CPU tasks quietly became our biggest bottleneck while scaling hundreds of LLM agents. The post Why Adding More AI Agents Made Our System Slower appeared first on Towards Data Science .