Why AI engineering needs old-school discipline

In this episode of The New Stack Makers, Nimisha Asthagiri of Thoughtworks explores why many AI initiatives stall between proof of concept and production. A key issue is that organizations focus on speed—asking how to move faster—rather than rethinking what new capabilities AI actually enables. Successful companies take a systems-thinking approach, investing in organizational literacy and aligning teams around meaningful use cases instead of retrofitting AI into existing workflows.

Asthagiri highlights that core engineering practices are ফিরে to prominence. As AI-generated code increases, so does the risk of “cognitive debt,” where developers lose understanding of their own systems. To counter this, teams are reviving fundamentals like test-driven development, mutation testing, observability, and zero-trust security, especially as autonomous agents contribute to production code.

She also introduces the concept of “dark code”—AI-generated code that may never be used—and argues for more intentional lifecycle management, including ephemeral code. Ultimately, the focus shifts from code itself to specifications, context management, and disciplined engineering practices.

Learn more from The New Stack around the latest about system-thinking approaches:

System Two AI: The Dawn of Reasoning Agents in Business

A practical systems engineering guide: Architecting AI-ready infrastructure for the agentic era

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