Anthropic's new playbook for AI-native software development outlines a six-stage framework for coding agents, but it stops short of addressing a crucial aspect of the process: the infrastructure that supports it. The playbook, which commits an artifact at each stage, leaves out the detail of what the agent's code runs against when it checks itself, a gap that can make or break the effectiveness of the entire process.
The framework has six stages: planning, design, build, deploy, and two stages that involve the agent checking its own work. Each stage produces an artifact that the next stage can read, from an intent.md file to a REVIEW.md file. However, the playbook does not specify what the agent's code should run against when it checks itself, a critical detail that can affect the accuracy of the results.
This omission is not a flaw in the playbook, but rather a reflection of the fact that every organization's infrastructure is unique. While Anthropic can provide guidance on how to structure the development process, it cannot dictate what the underlying infrastructure should look like. That is up to each organization to decide.
One company that is addressing this challenge is Metalbear, which offers a Kubernetes development platform called mirrord. Mirrord allows developers and AI coding agents to test code in a production-like environment before deploying it, by proxying traffic, environment variables, and files to and from a shared staging cluster. This approach enables organizations to create a more accurate and reliable testing environment, which is essential for making AI-native development work.
Mirrord is already being used by companies like monday.com, National Australia Bank, and SurveyMonkey to iterate and ship faster, while spending less on dev environment infrastructure. By providing a more realistic testing environment, mirrord helps to address the challenge of infrastructure in AI-native development, and enables organizations to get the most out of their coding agents.
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