TL;DR
Anthropic’s Claude Code team published a plain definition of agentic loops: an agent repeats work until a stop condition is met. Thorsten Meyer AI reframed the framework as a four-rung delegation ladder showing what users can stop doing themselves as autonomy increases.
Anthropic’s Claude Code team has published new guidance defining agentic loops as repeated work cycles that run until a stop condition is met, giving developers and businesses a clearer way to judge how much AI work to delegate and where human oversight should remain.
The source material cites Anthropic’s June 30, 2026 Claude blog post, “Getting started with loops,” by Delba de Oliveira and Michael Segner. It says Anthropic’s main definition is plain: a loop is an agent repeating cycles of work until a stop condition is reached.
Thorsten Meyer AI, in a July 1 AI Dispatch, reframes Anthropic’s loop types as a “delegation ladder”. The article says each rung is defined by what the user hands off: checking, deciding when work is done, starting the work, and eventually asking for the work in the first place.
The confirmed framework described in the source includes four loop types: turn-based skills, goal-based work, time-based loops or schedules, and proactive workflows with auto mode. Some features are described as research previews, and the source attributes the definitions and primitives to Anthropic while identifying the delegation-ladder framing as the author’s interpretation.
The delegation ladder: four agentic loops, and what each lets you stop doing
Strip the hype and a “loop” is simple — an agent repeating work until a stop condition is met. The useful lens isn’t the mechanics, it’s what you hand off. Four loop types = four rungs of delegation, from a tool you operate to a process that runs.
The whole framework reduces to one question about your own work: where am I the bottleneck, and which single piece can I hand off? Can you write the check? Is the goal concrete? Does the work arrive on a schedule? That answer picks your rung — and you climb one step at a time. The real skill isn’t operating a loop; it’s the judgment of what to delegate and how far — enough hands off to gain leverage, enough on the wheel that “runs without you” doesn’t become “runs away from you.”
AI Work Moves From Prompt To Process
The development matters because it gives teams a practical way to talk about AI autonomy without treating all agentic systems as the same. A turn-based loop still leaves the user in charge of each prompt, while a proactive workflow can run from an event or schedule without a human in real time.
For developers, the framework links autonomy to concrete controls: self-verification, evaluator checks, turn caps, schedules, and per-task goals. For businesses, the same structure clarifies where the human bottleneck sits and which part of the workflow can be handed off next.
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Four Rungs Of Delegation
The first rung, turn-based skills, keeps the user in control of each prompt but hands off more of the checking. The source describes an example where a front-end skill validates a UI change by running the dev server, clicking the new control, checking screenshots, reviewing console output, and running a performance trace.
The second rung, goal-based work, hands off the stop condition. A user can define a target, such as a performance score above 90, and set a turn cap. A separate evaluator model checks whether the goal has been met before the agent stops.
The third rung, time-based loops, hands off the trigger by starting work on an interval through local loop commands or cloud scheduling. The fourth rung, proactive workflows, hands off the prompt itself by letting event-driven systems orchestrate multiple agents under task-specific goals.
“A loop is an agent repeating cycles of work until a stop condition is met.”
— Thorsten Meyer AI Dispatch
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Limits Still Depend On Use Case
Several details remain dependent on implementation. The source says some features are research previews, so availability and behavior may vary across Claude Code environments and future releases.
It is also not yet clear from the source how broadly teams will adopt this framing outside Claude Code, or how reliably proactive workflows will perform across complex business processes. The article presents the delegation ladder as an interpretive model, not as an Anthropic product category.
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Teams Test The Next Rung
The next step for readers is to map current workflows to the ladder: identify where a person is still checking, deciding completion, starting repeated work, or writing recurring prompts. The source recommends starting with the simplest workable loop and climbing only when the task justifies more autonomy.
Teams adopting the model are likely to begin with skills and goal-based checks before moving scheduled or event-driven work into larger agent workflows. Cost controls, clear stop criteria, and review by a fresh-context agent are presented as ways to keep output quality and spending under control.
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Key Questions
What did Anthropic publish?
Anthropic’s Claude Code team published guidance defining loops as repeated agent work cycles that continue until a stop condition is reached.
What is the delegation ladder?
The delegation ladder is Thorsten Meyer AI’s framing of Anthropic’s loop types. It describes four steps where users hand off the check, the stop condition, the trigger, and finally the prompt itself.
Are these all new Claude Code features?
The source says the definitions, primitives, and examples come from Anthropic, while the delegation-ladder framing comes from the author. It also says some features are research previews.
Why should non-developers care?
The framework helps business teams decide where AI can reduce manual oversight without removing all human control. It turns agentic AI from a vague idea into a set of specific delegation choices.
What is the main caution?
The source’s main warning is that not every task needs a loop. Teams are advised to start with the simplest approach that works, then add autonomy only when the work calls for it.
Source: Thorsten Meyer AI
