This article shows how solving complex problems benefits from a team of AI agents with roles like planner, doer, tool operator, critic, supervisor, and presenter. It breaks down each subagent’s function and gives tips on prompting, model choice, tuning, and context setup. The CDN-Folk case illustrates how a team of agents designed, validated, and deployed a content delivery network faster than traditional methods.

• Splitting AI work across specialized agent roles (planner, doer, tool operator, critic, supervisor, presenter) beats using one monolithic LLM for complex tasks
• The CDN-Folk case cut build time to 3.5 days using this multi-agent approach and produced a cleaner architecture than legacy methods
• Matching model size to task complexity (small/fast for simple generation, larger chain-of-thought models for planning) and fine-tuning on domain data improves accuracy
• Giving agents narrowly scoped context (relevant APIs, logs, databases only) keeps them focused and reduces errors