When to Use Graph

Graph Engineering is worth it when you need control, not just cleverness.

𝗨𝘀𝗲 𝗶𝘁 𝘄𝗵𝗲𝗻:

• 𝗧𝗵𝗲 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗶𝘀 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱: mixing code, LLMs, tools, and humans with clear handoffs.
• 𝗦𝗮𝗳𝗲𝘁𝘆 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: approvals, policy checks, payments, deploys, writes to prod.
• 𝗧𝗮𝘀𝗸𝘀 𝗰𝗮𝗻 𝗿𝘂𝗻 𝗶𝗻 𝗽𝗮𝗿𝗮𝗹𝗹𝗲𝗹: fan-out/fan-in across independent subtasks.
• 𝗧𝗵𝗲 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗶𝘀 𝗹𝗼𝗻𝗴-𝗿𝘂𝗻𝗻𝗶𝗻𝗴: checkpointing, crash recovery, safe resume.
• 𝗬𝗼𝘂 𝗻𝗲𝗲𝗱 𝗮𝘂𝗱𝗶𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆: traceable steps, retries, provenance, and decisions.
• 𝗬𝗼𝘂 𝘄𝗮𝗻𝘁 𝗯𝗼𝘂𝗻𝗱𝗲𝗱 𝘀𝗲𝗹𝗳-𝗰𝗼𝗿𝗿𝗲𝗰𝘁𝗶𝗼𝗻: evaluator-optimizer loops with strict retry limits.

𝗔𝘃𝗼𝗶𝗱 𝗶𝘁 𝘄𝗵𝗲𝗻:

• 𝗧𝗵𝗲 𝘁𝗮𝘀𝗸 𝗶𝘀 𝗼𝗽𝗲𝗻-𝗲𝗻𝗱𝗲𝗱: research, exploration, ambiguous debugging.
• 𝗜𝘁’𝘀 𝘀𝗺𝗮𝗹𝗹: 1–3 steps usually don’t justify the overhead.
• 𝗜𝘁’𝘀 𝗱𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗶𝘀𝘁𝗶𝗰: plain code or a workflow engine is better.
• 𝗘𝘃𝗲𝗿𝘆 𝗯𝗿𝗮𝗻𝗰𝗵 𝗻𝗲𝗲𝗱𝘀 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝗵𝘂𝗴𝗲 𝗰𝗼𝗻𝘁𝗲𝘅𝘁: duplication kills the benefit.
• 𝗟𝗮𝘁𝗲𝗻𝗰𝘆/𝘁𝗼𝗸𝗲𝗻 𝗯𝘂𝗱𝗴𝗲𝘁 𝗶𝘀 𝘁𝗶𝗴𝗵𝘁: graphs multiply routing, state, and token costs.

𝗥𝘂𝗹𝗲 𝗼𝗳 𝘁𝗵𝘂𝗺𝗯:
Start simple. Add graph structure only when it measurably improves reliability, safety, or throughput.

𝗤𝘂𝗶𝗰𝗸 𝘁𝗲𝘀𝘁:

• 𝗙𝗮𝗹𝘀𝗲 𝗲𝗱𝗴𝗲? If Step B doesn’t truly need Step A’s output, don’t chain it.
• 𝗙𝗮𝗶𝗹𝘂𝗿𝗲 𝗶𝘀𝗼𝗹𝗮𝘁𝗶𝗼𝗻? If one model mistake can derail everything, put that step behind a bounded node with retries/fallbacks.

Use Graph Engineering for 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗮𝗯𝗹𝗲, 𝗮𝘂𝗱𝗶𝘁𝗮𝗯𝗹𝗲, 𝘀𝗮𝗳𝗲𝘁𝘆-𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹, 𝗽𝗮𝗿𝗮𝗹𝗹𝗲𝗹, 𝗼𝗿 𝗱𝘂𝗿𝗮𝗯𝗹𝗲 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀.
Skip it for 𝘀𝗺𝗮𝗹𝗹, 𝗱𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗶𝘀𝘁𝗶𝗰, 𝗼𝗿 𝗵𝗶𝗴𝗵𝗹𝘆 𝗲𝘅𝗽𝗹𝗼𝗿𝗮𝘁𝗼𝗿𝘆 𝘁𝗮𝘀𝗸𝘀.