AI coding assistants can generate pull requests faster than most teams can review them, and that mismatch is creating a new kind of bottleneck across engineering organizations. The volume of AI-generated code is growing rapidly, but without a reliable way to validate that code against real production environments, teams are left choosing between slowing down to manually review everything or accepting the risk of pushing untested changes forward.
Alan Shimel speaks with Sumeet Vaidya, CEO and co-founder of Crafting.dev, about the emerging concept of closed-loop autonomous development. The idea is straightforward: rather than treating AI agents as tools that hand off code for humans to verify, give those agents the ability to test their own output against live dependencies and real infrastructure before a human ever needs to get involved.
The conversation explores what it takes to make that work in practice. Traditional sandboxing approaches struggle to replicate the complexity of production environments, where services interact with databases, APIs and other systems in ways that simplified test setups cannot capture. Vaidya argues that the next step for AI-assisted development is not better code generation but better code validation, ensuring that what gets produced actually behaves correctly in context.
For DevOps and platform engineering teams, the implications are significant. If AI agents can reliably validate their own work, the role of human engineers shifts from gatekeeping individual pull requests to overseeing outcomes and setting guardrails. That changes how teams think about automation, testing pipelines and the division of labor between humans and machines in modern software delivery.
from DevOps.com https://ift.tt/coHLa9r
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