Agent Workflow Graph Lab

Graph Engineering is not “add more agents”

Graph Engineering designs control flow around an AI system. A workflow may contain deterministic application steps, retrieval, local agent loops, validation, review, and final actions. The graph decides how those responsibilities connect, not how impressive the diagram looks.

Loop Engineering and Graph Engineering operate at different levels. A loop defines how one local unit iterates through action, observation, verification, retry, recovery, escalation, and stopping. A graph decides where that local loop sits relative to other nodes and branches.

Retry scope changes the failure blast radius. Restarting an entire workflow because one node failed duplicates work that already succeeded. A node-level retry keeps recovery local and makes side effects easier to reason about.

Shared mutable state reduces plumbing but increases coupling. One stale observation can contaminate branches that never depended on the failing node. Isolated branch state requires an explicit merge, creating a useful validation boundary.

Parallel branches can shorten a critical path when work is genuinely independent. They also add scheduling, duplicated setup, synchronization, and join decisions. First-result joins are fast; verified joins trade extra work for stronger evidence.

More agents are not a goal. Extra workers are useful only when they represent independent capabilities or contexts. Deterministic code is often the better node when a responsibility has a stable contract.

Review placement is also topology. A review step everywhere creates a bottleneck. A gate at the consequential boundary protects the important action while leaving low-risk work automated. All values in this Lab are deterministic educational metrics rather than production benchmarks.