A Senior Engineer's Framework for Knowing When Not to Use AI in Products
A senior software engineer presents a practical decision framework for determining when NOT to use AI in product development. The framework centers on two key dimensions: specification clarity (clear vs. unclear) and consequence severity (low vs. high). When specs are clear and consequences are low, AI agents can be safely deployed. But when specs are unclear and consequences are high, engineers should avoid AI and rely on traditional deterministic approaches. The article provides honest guidance on AI's real limitations in production environments.
Key quotes
Clear spec, low consequence: let the agent run.
Unclear spec, high consequence: put the tool down.
A senior engineer's honest map of AI's real limits in product development.
From the article
You might also wanna read
The Case for AI Agents That Can Say 'No': Why Software Development Needs Meaningful Conversations Over Isolation
The article critiques the software industry's rush to build AI agents that always say 'yes' to requests, arguing that sometimes the correct
systemic.engineering·4mo agoThree Years In: A Senior Engineer's Reflection on AI's Impact on the Software Development Role
A senior engineer reflects on the long-term sustainability of AI tools in software development, three years into deep organizational adoptio
Three Years In: A Senior Engineer's Reflection on AI's Impact on the Software Development Role
A senior engineer reflects on the long-term sustainability of AI tools in software development, three years into deep organizational adoptio
The Growing Importance of Formal Specification in AI-Driven Software Development
The article discusses the evolving role of software engineers in an AI-driven development landscape, arguing that while initial predictions
AI in software engineering demands stricter discipline, not relaxed standards
The article argues that AI adoption in software engineering requires more discipline, not less. Drawing parallels to the shift from handcraf
AI in software engineering demands stricter discipline, not relaxed standards
The article argues that AI adoption in software engineering requires more discipline, not less. Drawing parallels to the shift from handcraf
The Risks of Over-Reliance on AI for Software Architecture Decisions
A critical analysis of how organizations are over-relying on AI tools like Claude, ChatGPT, and Copilot for high-level architectural and str


Comments
Sign in to join the conversation.
No comments yet. Be the first.