Agent Workflow Controller is a skill built for developers and AI orchestrators who need to manage complex, multi-step, or multi-agent tasks. Rather than relying on a single monolithic prompt, it transforms vague requests into structured execution plans with defined phases, roles, and boundaries — acting as an operating system layer for agent logic.
The core problem it addresses is agentic drift: the tendency of LLM-based agents to duplicate work, produce unverifiable outputs, or break down during handoffs between agents. This skill counters that by enforcing ownership rules, explicit exit gates, and evidence-based verification. Every output claim is tied to specific evidence such as logs, diffs, or citations before a workflow phase can proceed.
Four workflow patterns are supported. Discovery-Execute-Verify handles tasks where initial state is unknown. Approval-Gated is designed for high-risk, destructive, or financial operations that require explicit sign-off before proceeding. Parallel Slice manages independent sub-tasks with disjoint ownership to avoid conflicts. Failure-Repair Loop provides systematic debugging support when a process fails and needs structured recovery.
The skill produces a comprehensive Workflow Plan artifact containing a mission statement, complexity classification, a role and purpose matrix, ownership maps covering files and decisions, and concrete verification plans. It is not a runtime execution engine — it produces plans and structures, which an agent or orchestrator then follows. This distinction matters when evaluating whether it fits a given architecture.