The End of ‘It Works on My Machine’: How AI is Erasing Your Biggest Project Bottleneck

A new tool from Anthropic promises to slash developer setup time and eliminate environment-related bugs by moving the entire coding process to the cloud. With the potential to run multiple development tasks in parallel, this isn’t just a new toy for coders—it’s a fundamental shift in how project managers need to think about their delivery pipeline.

For every project manager who has heard the dreaded phrase, “but it works on my machine,” a moment of liberation may be at hand. The complex, time-consuming, and often frustrating process of setting up and maintaining local development environments has long been a hidden tax on project timelines—a source of friction, delays, and bugs that are notoriously difficult to squash. But what if the developer’s local machine was no longer the centre of the universe?

Anthropic, a key rival to OpenAI, has just thrown a grenade into that old paradigm with the launch of Claude Code on the web. This isn’t just another AI code-completion tool. It’s a browser-based, cloud-powered environment where developers can delegate entire coding tasks to an AI agent that operates in its own secure, sandboxed workspace [1]. By connecting to a GitHub repository, a developer can simply describe a task—from fixing a bug to implementing a new feature—and the AI gets to work, with no local setup required.

For project professionals, this development is more than just a technical curiosity. It represents a potential re-architecting of the software development lifecycle and a powerful new lever for accelerating project delivery.

The Frictionless Development Environment

The beauty of Claude Code lies in its simplicity and power. It allows a developer to kick off multiple, parallel coding sessions across different repositories, all from a single web interface. Each task runs in a perfectly clean, isolated environment, eliminating the “works on my machine” problem entirely. The AI handles the implementation, runs tests, and, when it’s done, automatically creates a pull request with a clear summary of the changes [1].

This is a game-changer for project velocity. Think of your bug backlog. Instead of a developer tackling tickets one by one, they could potentially assign a dozen routine bugs to a swarm of Claude Code agents running in parallel. Think of routine maintenance, dependency updates, or refactoring tasks—the kind of work that consumes valuable developer time but delivers little immediate feature value. This can now be offloaded, freeing up your most valuable resources to focus on complex, high-value problems.

“With Claude Code running in the cloud, you can now run multiple tasks in parallel across different repositories from a single interface and ship faster with automatic PR creation and clear change summaries.”

— Anthropic Announcement [1]

This shift from sequential to parallel execution of development tasks is a profound one. It requires project managers to think less like supervisors of a linear production line and more like orchestrators of a distributed, on-demand workforce.

Security in a Sandboxed World

After the security alarms raised by AI browsers like OpenAI’s Atlas, it’s natural to be sceptical of any tool that gives an AI agent access to your codebase. However, Anthropic appears to have put security at the forefront of Claude Code’s design. This is not an AI running amok on a developer’s machine.

Every task is executed in an isolated sandbox with strict network and filesystem restrictions. Git interactions are handled through a secure proxy, ensuring the AI can only access authorised repositories [1]. Furthermore, teams can implement custom network configurations to control exactly what domains the AI can connect to—for example, allowing it to download approved packages from npm but blocking access to anything else. While no system is perfectly invulnerable, this “security-first” approach is a critical prerequisite for enterprise adoption and a welcome sign of maturity in the agentic AI space [2, 3].

“Every Claude Code task runs in an isolated sandbox environment with network and filesystem restrictions. Git interactions are handled through a secure proxy service that ensures Claude can only access authorized repositories.”

— Anthropic Announcement [1]

The New Role for Project and Product Managers

This tool doesn’t make developers—or their managers—obsolete. It changes their roles. The premium is no longer on the manual, line-by-line writing of code for routine tasks. The premium is now on the ability to clearly and unambiguously define the task to be done. The quality of the prompt or the task description given to the AI will directly determine the quality of the output [4].

For project managers, this means a renewed focus on the fundamentals: writing crystal-clear user stories, defining precise acceptance criteria, and ensuring that every task in the backlog is a well-defined, testable unit of work. Your role shifts from simply tracking progress to becoming the master curator of the work pipeline that feeds the AI [5]. You are the human intelligence that directs the artificial workforce.

Furthermore, the output still requires human review. The AI generates a pull request, but it is a human developer who must review it, test it, and ultimately approve the merge. The process becomes one of collaboration between human and machine, with the AI handling the laborious “how” and the human providing the strategic “what” and the final quality assurance [6].

At Project Flux, we see this as a powerful evolution. It’s a tool that, if managed correctly, can dramatically reduce waste, shorten feedback loops, and increase the overall velocity of a project. It allows teams to spend less time on the mechanics of coding and more time on the strategic thinking that delivers real value.

Are you ready to manage a hybrid team of human and AI developers? The tools are here. Subscribe to Project Flux to gain the frameworks and insights you need to integrate this new generation of AI into your projects and supercharge your delivery.

References

[1] Anthropic. (2025, October 20). Claude Code on the web. https://www.anthropic.com/news/claude-code-on-the-web

[2] InfoWorld. (2024, May 21). Secure sandboxing for AI agents. https://www.infoworld.com/article/3715331/secure-sandboxing-for-ai-agents.html

[3] TechTarget. (2024, September 10). Why sandboxing is critical for AI workload security. https://www.techtarget.com/searchsecurity/tip/Why-sandboxing-is-critical-for-AI-workload-security

[4] GitHub. (2023, March 22). The art of the prompt: How to get the best out of generative AI. https://github.blog/2023-03-22-the-art-of-the-prompt-how-to-get-the-best-out-of-generative-ai/

[5] McKinsey & Company. (2023, June 26). The product manager’s guide to the age of AI. https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/the-product-managers-guide-to-the-age-of-ai

[6] IEEE Spectrum. (2024, January 18). AI Is Not Going to Replace Programmers. https://spectrum.ieee.org/ai-wont-replace-programmers