Graph Engineering explained: what it is, when to use, when not to
This article frames multi-agent workflow design as 'graph engineering': nodes do bounded jobs with defined contracts, edges carry real outputs, and a 'fake-edge test' exposes unnecessary waits in linear chains. The core pattern is the diamond—fan out parallel research, reduce with plain code, synthesize with one final agent—plus a strict rule that verifiers must run on fresh context, never the worker's chat. It includes copy-paste specs for Claude Code dynamic workflows (triggered by the word 'workflow') covering research, SEO drafting, go-to-market, refactoring, and discovery loops, along with three failure modes: context collapse, false independence, and silent node failure. It also cites honest economics: the Bun rewrite example ran ~50 workflows with up to 64 agents over 11 days, costing roughly $165,000 and requiring heavy human supervision. For engineers already using Claude Code and looking to move from sequential prompts to parallel orchestration, this is a useful but beginner-leaning guide.