agentic-development

Managing parallel AI agents involves orchestrating multiple agents within a single directory to execute tasks simultaneously. This workflow optimization simplifies complex orchestration and improves software development efficiency.

Writing prompts for AI coding agents requires concise instructions and robust context sharing, such as using screenshots. Effective prompt engineering focuses on providing clear context to improve agent output quality and task accuracy.

Choosing AI models and tools for agentic development requires understanding the trade-offs between different options, such as using CLIs versus MCPs. Evaluate model strengths and tool capabilities to match your specific software development needs.

Core principles for building software with AI agents include 'Think in Blast Radius', 'Context Is King', and 'Just Talk To It'. These concepts guide efficient AI-assisted development by emphasizing impactful changes and clear communication.

Low quality output from an AI agent often stems from insufficient context or overly complex prompts. Improving agent output quality requires adopting concise prompting and ensuring the model receives clear, relevant context.