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. ```mermaid flowchart LR A[Monolithic Custom agent<br/>Rules + logic + state + outputs] B[Thin orchestrator Custom agent<br/>Sequencing + policy] C[Focused Skill units<br/>Executable capabilities] D[Scoped Instructions<br/>Standards by applyTo] E[Stable output contracts] F[Maintainable AI workflow] A --> B B --> C B --> D C --> E D --> E E --> F style A fill:#ffd6d6 style B fill:#e1f5ff style C fill:#f0e1ff ...