Six Workflows That Make AI-Generated Code Actually Correct
AI coding agents are probabilistic; without deterministic guardrails, the same prompt produces different quality across runs. These six patterns give teams a repeatable workflow where correctness is checked by the machine, not by eyeballing the output.
The gap between generating code and generating correct code comes down to structure. A CLAUDE.md file acts as an onboarding manual that loads into every session, preventing the model from drifting on tech stack and conventions. Skills install domain-specific expertise per project, while hooks enforce deterministic rules outside the model—rejecting a commit that includes an .env file or auto-running a compile-and-fix loop until tests pass.
Visual tasks benefit from splitting the prompt into static description and dynamic interaction. Context is the most expensive resource in an agent session, so global Skills and MCP tools create noise; per-project configuration and manual /compact calls keep the context budget under control. A good prompt names the target file, the action, and the verification method, which raises execution accuracy sharply.
Anthropic's newly released Fable 5 model, available free for Pro and Max users until June 22, handled a multi-step PPT design task by fetching a third-party Skill, ingesting project materials, and producing a polished deck with minimal token waste.
CLAUDE.md and Skills are still prompt-level suggestions—the model can ignore them under enough pressure. Hooks are the first mechanism that enforces rules deterministically, which changes the reliability ceiling for agent-generated code.
Context is the real pricing unit in agent workflows, not tokens per request. Every global Skill or MCP tool you install is a permanent tax on every session, so per-project scoping is a cost-control measure, not just an organizational preference.
The three-element prompt structure (target, action, verification) is effectively a mini specification. It shifts the model from 'write some code' to 'produce an artifact that passes a test,' which is the difference between a demo and something deployable.