Agentic Loops for Knowledge Workers

Key Takeaways

• Not everything should become a loop. Avoid loops when one good model pass is enough, when the task has no objective finish line, or when the core value lies in human judgement, taste, interpretation or accountability rather than repeated autonomous execution.
• A useful loop needs a checkable goal, bounded scope and a task that can converge. The agent should know what “done” means, be able to measure progress toward it, operate within clear limits, and stop when success or a fail-safe condition is reached.
• A node is a unit of work; a graph describes how work moves between units. Nodes can represent agents, tasks or processing steps, while the edges define hand-offs, dependencies and feedback. A loop is therefore the simplest possible graph: one node whose output feeds back into itself.
• Work graphs become useful when one loop is no longer enough. They allow larger jobs to be split into distinct responsibilities such as generation, research, checking, synthesis or review, with explicit hand-offs between them rather than relying on one agent to do everything.
• There are five practical levels of work graphs, and more complexity is not automatically better: 1. Agent-created sub-agents — let an agentic system dynamically break work into sub-tasks. 2. Natural-language graphs — describe the roles, sequence and hand-offs directly in instructions. 3. Persistent agents and reusable skills — create recurring specialist roles or procedures that can be reused across work. 4. Visual workflow graphs — explicitly design the workflow in orchestration tools. 5. Code-defined graphs — implement the graph programmatically when greater control, branching, state management or reliability is required. The practical rule is to use the simplest level that reliably does the job.