The AI-Native SDLC Starts with Your Infrastructure
Anthropic has released a playbook for integrating AI coding agents into the software development lifecycle (SDLC). The framework emphasizes that infrastructure, specifically local testing and build capabilities, is the critical prerequisite for successful agentic automation.
Why it matters
As organizations shift toward AI-native development, understanding the technical constraints and infrastructure requirements is essential for maintaining code quality and reliability.
Anthropic published a playbook for restructuring the software lifecycle around coding agents. Its premise is that the traditional SDLC was designed when writing code was the slow part, agents made that part fast, and the constraint moved to the stages around it.
The framework has six stages, and each one commits an artifact the next stage can read. Planning produces an intent.md . Design turns that into a spec.md . Build produces a plan.md before any code is edited. Deploy puts the review policy in a REVIEW.md .
It is more specific than most process documents, but it also leaves out an important detail that can decide whether the rest of the process actually works, which is what the agent's code runs against when it checks itself.
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