At the Core of an Agent Loop Are Triggers, Stop Criteria, and Verification
An Agent that cannot terminate in a controlled way is merely an expensive state-polluting machine; the core of building a reliable system is encoding triggers, stop criteria, and verification standards explicitly into its contracts.
The Claude Code team's technical talk *Getting started with loops* redefines a Loop as a controlled unit of work. Reading through it, that definition is what resonated with me most; the specific command-line tools came second.
When people first start working with Agents, it is easy to fall into a certain intuition: as long as a model can keep running on its own and calling tools repeatedly, the system has achieved "autonomy." I have my doubts about that intuition. Any Agent that cannot terminate in a controlled way will become an expensive and dangerous state-polluting machine.
If a system blindly drops a model into a loop without clearly defining when to start, when to stop, what objective metrics to use for acceptance, or how to trip a circuit breaker upon failure, it rarely delivers productivity. Instead, it leads to runaway quota consumption, polluted workspace files, and automated noise that is painful to debug.
To upgrade a one-off Prompt into a reusable, deliverable working system, I believe the primary task is to take the steps humans used to judge by feel and encode them explicitly as hard system contracts: triggers, stop criteria, and verification.
Three Core Questions Behind a Controlled Loop
When evaluating whether an Agent Loop is mature, I look at how it answers three concrete questions:
- Trigger: What starts the system? Is it a single prompt typed by a user in the terminal, a scheduled task running at fixed intervals (schedule/cron), or an event notification emitted by an external system (such as a GitHub Webhook, a PR status change, or a message queue)?
- Stop Criteria: On what basis does the system stop? Are all unit tests passing, has an objective score reached a threshold, have all code review comments been resolved, or has it hit a hard maximum retry limit (turn cap)?
- Verification: How does the system prove it actually got the job done? Does it read the return value of an actual command, execute interactions in a browser and take screenshots, rely on a second independent Evaluator Agent to score the result, or must it wait for human confirmation?
If these three questions are not explicitly defined in code and rules, any so-called "autonomous Agent loop" will inevitably degenerate into a game of chance in practice.
Four Levels of Delegating Control
In *Getting started with loops*, the author categorizes common engineering Loops into four types. Based on my understanding, these four forms represent an evolutionary progression where control is gradually handed over from humans to the system:
| Loop Type | Control Handed to the System | Typical Use Cases | Primary Engineering Risks |
|---|---|---|---|
| Turn-based (Single-turn) | Local execution and self-checks within a step | Solution exploration, quick fixes, short-horizon tasks | Relies on human visual inspection; easy to miss real issues in integrated environments |
| Goal-based (Goal-driven) | Autonomous judgment of termination conditions | Medium- to long-horizon tasks with clear acceptance assertions | If the objective function is skewed, the Agent will go all out optimizing the wrong metric |
| Time-based (Scheduled cadence) | Trigger cadence and polling intervals | Monitoring PR/CI, fetching data sources, syncing workspace state | If polling frequency exceeds how often external information updates, it burns quota and creates redundant context |
| Proactive (Autonomous long workflows) | End-to-end complete workflows | Dependency upgrades, fixing known Issues, routine migrations | Excessive permissions, context distortion from repeated compaction, and side effects on the environment from missteps |
I do not think a later stage on this list is inherently more advanced. For many tasks, the safest approach is to start with the simplest Turn-based setup. Only when a task has clear acceptance logic and solid test coverage do I recommend upgrading it to Goal-based or Time-based.
Verification Is the Foundation of a Loop
When reading through these designs, my biggest concern is seeing complex scheduling logic built too early while underlying verification capabilities are set aside.
A Loop has no judgment of its own; it only faithfully amplifies the existing characteristics of the underlying system. If a project has a clean codebase, rigorous test coverage, and sound specification docs, a Loop can repeatedly amplify that determinism and save a lot of time. Conversely, if a project has vague acceptance criteria, a build environment that errors out constantly, and messy permission boundaries, a Loop will rapidly spread that chaos across the entire workspace.
That is why I prioritize writing a clear Verification Skill before building any automated scheduling.
Take a common frontend change as an example. We cannot simply ask the Agent to "fix the button styling" and call the turn finished. A reliable verification flow should be quantifiable and executable:
- Start the local dev server and open the target page in a browser;
- Simulate real user click interactions, confirm state transitions work properly, and take before-and-after screenshots as evidence;
- Check the browser Console to ensure no new Warnings or Errors were introduced;
- Run the relevant test suites to confirm existing functionality remains intact.
The more the verification logic can be executed objectively by a machine to produce concrete evidence, the less the Agent needs to rely on semantic guesswork to wonder "Am I done yet?", allowing it to exit decisively once the stop criteria are met.
Which Tasks I Prioritize Turning into Loops
Tasks that are good candidates for turning into automated Loops first usually share three characteristics:
- Stable input sources: such as structured commit logs (commit), continuous integration failures (CI), pull requests (PR), or scheduled data feeds;
- Objective acceptance criteria: such as all unit tests passing, files updated, or static analysis running without errors;
- Isolation buffers for high-risk actions: when operations involve production deployments, deleting data, or external payments, the system must pause and wait for final human confirmation.
Boundaries and Human Guardrails
Not every task belongs inside a fully automated Loop.
My view is that when dealing with the following scenarios, we must maintain strict circuit-breaker breakpoints and human judgment (Human in the loop) within the Loop:
- Subjective and aesthetic judgment: visual interface style, brand tone, or the emotional leaning of copywriting;
- Core business and strategic trade-offs: compromises in business direction, architectural refactoring roadmaps, or choices in investment logic;
- High-risk, irreversible actions: production code deployments, tearing down cloud resources, modifying sensitive permissions, or making external payments.
A mature Loop system should take over the heavy lifting of mechanical, tedious, repeatable work that has clear verification criteria, and then pause with a complete chain of evidence when it hits boundary nodes requiring value judgment, handing the final decision to a human. Less human involvement does not, by itself, mean a system is more mature.