Agentic loops are suitable for tasks where the output is binary (done or not done) and creativity is not required. Generating hundreds of SEO pages from a fixed template is a prime example where automation excels, unlike building a unique user-facing application.

Rather than complex orchestration, Anthropic's Boris Cherny relies on a simple `/loop` command, which uses cron to schedule recurring agentic tasks. He uses dozens of these loops for everything from auto-rebasing PRs to clustering user feedback, suggesting simplicity is key for powerful agentic workflows.

The buzzwords "loop engineering" and "graph engineering" are not distinct concepts but part of a continuum. The textbook definition of a loop is a graph with a single node pointing back to itself. This understanding demystifies the transition to more complex, multi-agent systems.

The concept of an AI 'loop' is an evolution, not a revolution. It applies traditional, time-tested automation triggers—such as scheduled cron jobs or event-driven webhooks—to initiate and control modern AI agents, providing a familiar foundation for developers.

Unlike simple chat models that provide answers to questions, AI agents are designed to autonomously achieve a goal. They operate in a continuous 'observe, think, act' loop to plan and execute tasks until a result is delivered, moving beyond the back-and-forth nature of chat.

A simple test differentiates agent vs. agentic needs: if you can define the exact sequence of actions beforehand, build a simple AI agent. If the next step depends on the previous step's unpredictable outcome, you require a more complex agentic system.

A subtle failure mode for agentic loops is when a task is marked "done" because it met the literal finish line, but the output is bland. This isn't the agent's fault; it's a failure of the user to properly define the goal with sufficient quality criteria.

Agent loops are a new method where a user provides a high-level goal (e.g., 'create my monthly budget') instead of discrete instructions. The AI then autonomously plans, executes, and iterates in a loop until the objective is met, requiring far less manual human intervention and prompt engineering.

Agentic loops are not a universal solution. They are most effective in domains where success can be measured by a clear, objective score and where failed experiments are cheap and quick. This framework helps identify the best business processes to automate, starting with areas like code generation or ad testing, not subjective, slow-moving tasks like political negotiation.