arXiv:2604. 00073v3 Announce Type: replace-cross Abstract: There has been growing interest in building agents that can interact with digital platforms to execute meaningful enterprise tasks autonomously.
Giving an AI agent access to a data warehouse doesn't automatically make it agent-ready. The real challenge lies in teaching the agent what the data means and when it's reliable enough to use.
The article outlines five principles that guide the successful deployment of enterprise agent systems, illustrated with a real-world example from a $100M+ company. It explains how these principles help ensure that such systems can be trusted, verified, and improved over time. The post serves as a practical guide for building reliable agent-based solutions in production environments.
arXiv:2607. 01418v1 Announce Type: cross Abstract: Organizations rolling out agentic command line tools like Anthropic's Claude Code and GitHub's Copilot CLI need to know who will try them, who will keep using them, and whether the tools produce enough output to justify their cost.
The article explains how the five core assumptions of MLOps monitoring are violated when agents are deployed to production, leading to inherited signals that incorrectly mark failed runs as healthy. It highlights the specific ways in which agent-based systems disrupt traditional monitoring stacks and the implications for reliability and performance. The piece serves as a warning for practitioners transitioning from MLOps to AgentOps, outlining the critical monitoring gaps that arise.
What our over-dependence on external consulting teaches us about delegating our minds to machines The post The Big Con of Agentic AI appeared first on Towards Data Science .
Increasing enterprise support with more security features and controls, updates to our Assistants API, and tools to better manage costs.
arXiv:2512. 04123v4 Announce Type: replace-cross Abstract: LLM-based agents already operate in production across many industries, yet we lack an understanding of what technical methods make deployments successful.
A data-driven look at enterprise AI adoption, showing how organizations move from experimentation to real productivity gains and new capabilities.
arXiv:2506.03828v4 Announce Type: replace Abstract: AI for Industrial Asset Lifecycle Management aims to automate complex operational workflows, such as condition monitoring and maintenance schedulin...