Can an LLM read a 30+ page Standard Operating Procedure (SOP) and produce a team of agents that reliably execute it? Industrial SOPs bury workflow logic in prose, leave inter-step dependencies implicit, and scatter referenced entities across sections, forcing planners to parse, sequence, and tool-map within one context window. We argue that the missing primitive is procedural chunking: decomposing a document into self-contained executable units. Unlike RAG-style chunking, each chunk must be independently executable and chunks must jointly form a valid DAG encoding data and process dependencies, turning a prose SOP into a compact orchestrable workflow graph. We present ProChunker, a three-phase self-optimizing LLM pipeline (Boundary Detection, Metadata Extraction, Dependency Construction) with Stochastic Best-of-N prompt exploration guided by five custom metrics (Boundary Clarity, Entity Reference Coverage, Agent Self-Containedness Agreement, Step Coverage, Graph Validity); we then fine-tune Qwen3-1.7B on the pipeline’s own phase outputs, cutting latency by ∼27 min. Across three corpora (500 BioProBench protocols, six Industrial SOPs, 14 SOP-Bench documents), ProChunker improves the five-metric composite over the strongest baseline by +0.036 to +0.111 across corpora. Our study shows ProChunker increases agent construction success rate by +17% and resolves >90% of procedural ambiguity.