Avoiding Monolithic AI Workflow Design: Better Structure for Custom Agents, Instructions, and Skills
When AI-assisted workflows start delivering value, the first version often grows in the wrong direction: one huge Custom agent with every rule, every step, and every edge case embedded inline. It works, until every small change becomes slow and starts breaking something unrelated. This post is about structural mistakes to avoid, and a practical architecture that scales: thin orchestrator agents, focused Skill units, clear Instructions , and predictable output paths. Monolithic Custom agent Rules + logic + state + outputs Thin orchestrator Custom agent Sequencing + policy Focused Skill units Executable capabilities Scoped Instructions Standards by applyTo Stable output contracts Maintainable AI workflow 1. What goes wrong in monolithic agent design In practice, many failures here are not model failures. They are boundary failures. When one Custom agent owns orchestration, domain logic, schema shaping, state management, approval flow, and output formatting, e...