Loops and beads: orchestrating AI agents with Postman

AI agents commonly follow a decide-act-observe cycle, but their performance depends on whether work is organized as sequential loops or dependency-based “beads.” Loop agents use an ongoing model conversation in which each tool call and result informs the next step, making them suitable for open-ended or conversational tasks where the model may need to change direction. Bead agents instead use a predefined graph of small functions with explicit inputs, outputs, and dependencies, allowing independent steps to run concurrently and enabling targeted retries when individual steps fail. Using a Postman API health-check task as an example, the author compares a loop that sequentially retrieves a collection summary and runs a monitor with a bead graph that performs both operations in parallel before producing a report. In a simulated test, the loop took about 3.02 seconds while the bead approach took 1.83 seconds, reflecting that parallel work completes near the duration of the slowest operation rather than the sum of all operations. The comparison suggests that loops remain valuable for iterative tasks such as debugging or responding to evolving findings, while bead graphs are more efficient for predictable workflows such as independently testing, auditing, documenting, or scoring an API.

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