loop-vs-graph-decisionlisted

# Loop vs. Graph: The Decision **Source**: AI Builder Club, "Graph Engineering Guide (2026)" and "Graph Engineering vs Loop Engineering" — synthesized practitioner content (not an academic source; the guide itself names its own inputs, including X/Twitter commentary from Peter Steinberger, @svpino, @rohit4verse, and critics like David K. Piano and Pawel Huryn who argue the underlying mechanics — directed graphs, state machines — are decades-old CS, not new capability). ## The default: try keeping it a loop first Per the guide's own implementation checklist, step one is **"try keeping it a loop first."** A loop — discover → plan → execute → verify → repeat — handles a large fraction of agentic work. Escalating to a graph adds real, non-trivial cost: multiple prompts to maintain instead of one, a state schema to define between nodes, and new failure modes (silent state leaks between nodes, infinite routing loops, merge bugs). Reach for a graph because the task's shape demands it, not because it sounds more sophisticated. ## When a loop is the right call - One job with a clear finish line - Sequential steps only, no genuine parallelism - The same tools/model throughout - One agent can safely roam the whole task - Self-verification (or a single verifier) is adequate for the stakes involved ## When a graph is the right call - Work genuinely splits into distinct specialties with real handoffs - Parallelism is needed — fan-out then join, not just sequential steps drawn as box