I Built This to Fix One Task. It Turned Into Something You Can Run.
dev.to·
Building reliable AI-assisted workflows requires moving away from open-ended conversational context toward structured, artifact-driven state pipelines. This write-up details a production-tested agent harness architecture governed by a root classification instruction file and seven underlying specialized skill modules. Instead of relying on sprawling chat histories, each phase executes sequentially by reading artifacts produced by the preceding stage. The system relies on foundational operational plumbing, including standardized slug identifiers per task, an explicit requirement manifest separating raw user requests from model inferences, strict exit gates preventing unchecked transitions, and documented waivers whenever a phase is intentionally bypassed. For backend developers evolving toward staff-level systems design, this pattern provides a clean template for building deterministic, auditable, and maintainable automation workflows out of non-deterministic LLM components.