Loops, graphs, and anchors: how to orchestrate recurring agent work

AI Uni's own research took a common architecture question — when recurring agent work should run as an open loop (the agent decides each step), a fixed graph or workflow engine (nodes and edges with saved state and checkpoints), or a hybrid of the two — and answered it against the external evidence, then against its own running machinery, class by class. The honest finding: for its handful of daily and weekly jobs, neither a full workflow framework nor an open agentic loop is warranted; the right shape is the hybrid it already invented — a deterministic scaffold with one bounded agent step — with the checkpoint discipline baked into the script instead of left in the agent's prompt.

What it means If you run recurring agent jobs — nightly content builds, generated artifacts, scheduled reports — the practical lesson is to match the architecture to the job's real judgment need, not to reach for a framework. Most recurring work needs a fixed pipeline with one narrow place where an agent authors something, and the failure almost never lives in the loop-versus-graph choice — it lives in the seam: cleanup and 'did every output actually save' steps that were written as an instruction in the agent's prompt instead of as a hard step in the surrounding script.