In the last few years, large language models (LLMs) have moved from research labs to production systems powering critical business functions. This rapid adoption poses a fundamental challenge for enterprises: How do you deploy AI with confidence when models can behave unpredictably under adversarial conditions? The question keeping IT leaders awake isn't if their AI will fail—it's when, and what will the consequences be?As we've already discovered, traditional software testing approaches fall short when applied to AI. Models don't just have bugs that can be discovered and quickly patched, th
The promise of enterprise AI agents is straightforward: Let the model think, Let the code run, and keep everything under your control.Until now, this promise was hard to deliver. If you wanted Claude to write and execute code for your team, you had 2 options: Run everything on the cloud and accept that your data, your code, and your execution environment live outside your perimeter. Build the entire orchestration stack yourself and lose the intelligence that makes managed agents valuable.Anthropic's self-hosted sandboxes for Claude Managed Agents change that equation. Effectively, this capabil