In the static version, the edge list shows the branches and destinations. In this version, we read refund_flow to see them. The dynamic node guide covers this approach.
The human-review pause still works here. When the answer arrives, refund_flow runs again from the top, but completed ctx.run_node calls return their recorded outputs from session history. The lookups do not repeat, as direct function calls would. Keeping side effects inside child nodes lets completed calls replay their results when the parent resumes. Each child also gets its own trace span, and use_sub_branch=True keeps concurrent children’s events on separate branches.
For this fixed refund process, the edge list remains easy to inspect. The Python version gives us a place to add the investigation logic described above: inspect a result, choose a follow-up node, and run independent checks together. A model might suggest what to investigate, while code limits the work—for example, three follow-ups per finding and two levels of investigation before human review.
Loops can still fit in a static graph. A fixed draft → check → revise process can use a conditional back-edge and a router that limits revisions. “Static” describes the possible connections; the actual path and iteration count can vary. The graph guide covers conditional cycles.
You can also place a dynamic node inside a static workflow, using Python for a stage that needs it while keeping the surrounding process visible.
Choose who decides what runs next
Start with a question: can you draw the possible workflow before the input arrives? Include branches, loops, and repeated stages. You do not need to predict the path each request will take.
|
What you need |
Pattern |
| Independent jobs, then all their results | Fan-out and fan-in |
| A branch chosen by fixed rules | Deterministic router |
| A branch chosen by interpreting meaning | Agent router |
| A person’s decision before continuing | RequestInput |
| The same step across a list | Parallel worker |
| Code that schedules work as results arrive | Dynamic node |
Use an edge list when it makes those connections clear. Use dynamic orchestration when results create further work or Python expresses the control more naturally. A small, open-ended task may need only one agent and its tools.
In our refund workflow, the graph coordinates the lookups, code applies the policy, a person handles exceptions, and a model writes the reply. Graph engineering gives each a clear responsibility—and makes it easier to see how the process works.
