Feature Development
Systematic process for designing, implementing, and delivering new software capabilities.
Key Workflow for Long-Running AI Coding Agents
- Core Problem: AI agents (e.g., Claude) fail at “one-shot” development due to Context Window Limitations, causing incomplete or inaccurate code
- Anthropic-Adapted Solution: Iterative task breakdown with context management
- Break feature into atomic subtasks (max 10-20 lines of code)
- Execute one subtask per agent session
- Store intermediate results in Code Snippet notes for context continuity
- Validate each subtask
Karpathy Loop Engineering
For complex tasks, simple iterative breakdown is insufficient; agents require autonomous optimization loops to self-correct and improve output quality. This approach, detailed in Karpathy Loop Engineering: AI Agent Autonomous Optimization for Development, emphasizes structured workflows that allow AI agents to autonomously perform and refine tasks beyond single-prompt instructions.
- Autonomous Optimization: Agents iteratively refine their own output based on feedback loops, rather than just executing static subtasks
- Structured Iteration: Moves beyond simple task breakdown to include self-evaluation and correction phases
- Impact: Significantly improves code quality and reliability for complex feature implementation