Graph Topologies

In 𝗚𝗿𝗮𝗽𝗵 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴, execution topologies are the blueprint for how data, control, and state move through AI systems. They’re not just diagrams — they’re architecture patterns that make messy, non-deterministic workflows predictable, parallel, and testable.

𝟭. 𝗧𝘄𝗼 𝗖𝗼𝗿𝗲 𝗚𝗿𝗮𝗽𝗵 𝗧𝘆𝗽𝗲𝘀: 𝗗𝗔𝗚𝘀 𝘃𝘀. 𝗖𝘆𝗰𝗹𝗲𝘀

• 𝗦𝘁𝗮𝘁𝗶𝗰 𝗗𝗔𝗚𝘀: Best for fixed, known-in-advance workflows like

𝘚𝘱𝘦𝘤 → 𝘉𝘶𝘪𝘭𝘥 → 𝘛𝘦𝘴𝘵 → 𝘙𝘦𝘷𝘪𝘦𝘸 → 𝘋𝘦𝘱𝘭𝘰𝘺.
They enforce strict order, versioned plans, and auditability by banning loops.

• 𝗖𝘆𝗰𝗹𝗶𝗰 / 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗚𝗿𝗮𝗽𝗵𝘀: Real agent systems need retries, corrections, feedback, and iteration.

Cycles enable back-edges, while dynamic transitions let the graph spawn workers at runtime based on the input.

𝟮. 𝗖𝗼𝗺𝗺𝗼𝗻 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀

𝗔. 𝗙𝗮𝗻-𝗢𝘂𝘁 / 𝗙𝗮𝗻-𝗜𝗻

• One task splits into many parallel subtasks.
• Workers run independently.
• A join node or reducer merges results back into one state.
• Requires safe state reducers like append-only lists or set unions.
• Seen in LangGraph Send, Google ADK JoinNode, and Microsoft Agent Framework barriers.

𝗕. 𝗧𝗵𝗲 “𝗙𝗮𝗹𝘀𝗲 𝗘𝗱𝗴𝗲” 𝗧𝗲𝘀𝘁

• Ask: 𝗗𝗼𝗲𝘀 𝗦𝘁𝗲𝗽 𝗕 𝗿𝗲𝗮𝗹𝗹𝘆 𝗻𝗲𝗲𝗱 𝗦𝘁𝗲𝗽 𝗔’𝘀 𝗼𝘂𝘁𝗽𝘂𝘁?
• If not, the edge is fake.
• Break it into parallel branches instead of forcing a serial chain.

𝗖. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗼𝗿-𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲𝗿 𝗟𝗼𝗼𝗽𝘀

• A generator creates output.
• An evaluator checks it with tests, compilers, linters, or LLM criteria.
• Pass → publish.
• Fail → revise with feedback.
• Always bounded by retry limits and escalation paths.

𝗗. 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿-𝗪𝗼𝗿𝗸𝗲𝗿 𝗣𝗮𝘁𝘁𝗲𝗿𝗻

• A supervisor breaks a big task into smaller jobs.
• Specialized sub-agents handle the pieces.
• A single node can now represent a full autonomous agent, not just one model call.

𝗘. 𝗛𝘂𝗺𝗮𝗻-𝗶𝗻-𝘁𝗵𝗲-𝗟𝗼𝗼𝗽 𝗚𝗮𝘁𝗲𝘀

• High-risk actions like writes, payments, or deploys go through policy gates.
• The graph checkpoints state and pauses in waiting_human.
• Execution resumes only after explicit approval.

𝟯. 𝗗𝗼𝗺𝗮𝗶𝗻-𝗦𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗚𝗿𝗮𝗽𝗵𝘀

Graphs are also becoming knowledge layers for agents:

• 𝗧𝗮𝘀𝗸 & 𝗖𝗶𝗿𝗰𝘂𝗶𝘁 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻 𝗚𝗿𝗮𝗽𝗵𝘀 (𝗧𝗖𝗥𝗚): Links tasks to signals, rules, and waveforms in hardware design.
• 𝗖𝗼𝗱𝗲 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗚𝗿𝗮𝗽𝗵𝘀: Let coding agents query source code, call paths, and blast radius directly.
• 𝗘𝘅𝗲𝗰𝘂𝘁𝗮𝗯𝗹𝗲 𝗖𝗼𝗱𝗲 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲: Adds contracts, semantics, and test evidence to code units.
• 𝗦𝗵𝗮𝗿𝗲𝗱 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗠𝗲𝗺𝗼𝗿𝘆 𝗚𝗿𝗮𝗽𝗵𝘀: Give multiple agents one shared graph for history, entities, and reasoning traces.

Graph engineering turns AI workflows from ad hoc chains into 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱, 𝗶𝗻𝘀𝗽𝗲𝗰𝘁𝗮𝗯𝗹𝗲, 𝗮𝗻𝗱 𝘀𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 — with parallelism, feedback, governance, and shared memory built in.