My automated doubt development process
alexself.dev
June 7, 2026
8 min read
🔥🔥🔥🔥🔥
47/100
Summary
Automated doubt development involves critiquing the implementation of artifacts repeatedly to regain trust in AI-assisted development. This process aims to ensure adherence to standard engineering practices while using AI for writing code, specifications, and documentation.
Key Takeaways
- The automated doubt development process involves using specialized subagents to critique and audit the implementation of AI-generated artifacts, enhancing trust in AI-assisted development.
- The process begins with a design phase where a specification is created, followed by a pre-implementation workflow that includes multiple agents assessing design quality, completeness, and hidden assumptions.
- Iteration in the development process is determined by the scope of the project, with different sets of agents employed based on whether the scope is small, medium, or large.
- A companion checklist is generated to accompany the specification, aiding in the development process and ensuring all aspects are addressed.
Community Sentiment
Positives
- The integration of AI agents in the development process enhances collaboration by gathering diverse stakeholder input, which can lead to more comprehensive feature planning.
- Jumping into LLM development reflects a proactive approach to adapting to industry trends, suggesting that AI-assisted development could be a viable future path for software engineering.
Concerns
- There's a concern that relying on AI tools may lead to overengineering, as seen with Claude Code creating complex solutions for trivial issues.
- Trust issues arise when developers feel disconnected from the coding process, leading to skepticism about the effectiveness of AI in improving code quality.
