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A retry loop is not, by itself, an AI agent. The key question is whether the model can choose or revise actions in response to what it observes—or whether fixed code repeats a prescribed operation. A loop can be part of an agent, but the loop alone does not show that the system is acting agentically.
What makes an AI agent agentic?
There is no single industry certification or quantitative threshold for when software becomes an agent. The useful engineering distinction is decision authority: who determines the next step, and can that step change as the system receives new information?
Anthropic defines an agent as a model that directs its own processes and tool use to accomplish a task, deciding how to achieve the user’s goal rather than following a fixed script. In practice, that can mean planning, taking an action, observing the result, adjusting, and repeating until the task is done or human input is needed.
OpenAI’s practical guide draws a similar boundary: an application that uses an LLM but does not let it control workflow execution—such as a single-turn chatbot or sentiment classifier—is not an agent under that framing. Google’s glossary describes the cycle as Observe, Reason, Act, and Feedback, with a goal guiding the work. These are useful, complementary ways to describe agentic systems, not a formal universal standard.
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Is an AI agent just a loop?
Agents commonly run in a loop: the model receives context, proposes a tool call or other action, the runtime executes it, and the result is returned to the model. The OpenAI Agents SDK documents this kind of runner loop, which may continue through tool calls and handoffs until the model reaches a stopping point.
But the execution shape does not tell you who is steering. A fixed program can loop through the same request after an error without giving the model meaningful control. Conversely, a model-directed agent may operate inside a carefully bounded loop. The distinction is whether new observations can influence the model’s next choice—not whether the software repeats.
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How to distinguish a fixed workflow, an agent, and a hybrid
| Question | Fixed workflow | Model-directed agent | Hybrid design |
|---|---|---|---|
| Who chooses the next action? | Code follows a predefined sequence or branch. | The model selects among available actions or tools. | Code sets boundaries or stages; the model chooses within them. |
| Can the action change after feedback? | Only according to conditions explicitly encoded in the workflow. | The model can revise its next action based on observed results. | The model can adapt, but only within deterministic controls. |
| What guides progress? | Predetermined steps and conditions. | A goal or success criterion informs the model’s choices. | A goal is pursued through a mix of model decisions and fixed rules. |
| How does it stop or escalate? | Code-defined completion, failure, or timeout conditions. | The runtime or model identifies completion, a need for input, or another stopping condition. | Code enforces limits and escalation while the model can request a handoff. |
| Main trade-off | More predictable and controlled, but less flexible when circumstances vary. | More flexible, but its behavior depends on model choices and safeguards. | Balances adaptability with explicit limits; the exact balance depends on implementation. |
These categories are not mutually exclusive implementation types. Google Cloud’s architecture guidance discusses both agent and workflow patterns. A system can use a deterministic workflow to constrain what happens while still letting a model choose or adapt actions within that workflow. The important thing is to identify where decision-making authority actually sits.
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How to tell whether a system can actually adapt
Ask for a concrete account of the system’s control flow, then check its behavior against these questions:
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- Can the model select an action? Find out whether it can choose among tools or options, or whether code dictates the next operation.
- Can it change course? After a tool returns an unexpected result, can the model choose a different tool or strategy, or does the same request run again under the same conditions?
- What goal guides the choice? Look for a task outcome or success criterion, rather than an unbounded instruction to keep trying.
- What ends the run? Check for completion, failure, time or resource budgets, and escalation to a human when the system cannot proceed safely or confidently.
- Which layer makes each decision? Separate model choices from application-server logic, orchestration rules, tool permissions, and the environment where commands execute.
OpenAI’s Agents API architecture makes this separation visible: the model is not the entire system. The harness, tools and execution environment, and application server all shape what actions are possible and how results feed back into the process. A product’s label cannot tell you which layer controls a particular decision; its documentation or an observable trace can.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why retries and stopping rules matter
Retrying can be a sensible recovery mechanism—for example, when a temporary tool error might resolve on another attempt. It becomes a weak substitute for adaptation when the system repeats the same action without using the failure as information. A changed action after feedback is evidence of adaptation; a retry count alone is not.
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Stopping behavior is just as important as action selection. A useful system needs a way to recognize completion, halt on failure or limits, and ask for human input when necessary. The OpenAI Agents SDK for JavaScript documentation describes controls for ending a run on a first tool result and preventing repeated tool calls. These are runtime safeguards, not proof that a model is planning; they show how orchestration can bound or terminate a loop.
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Does agentic mean reliable or safe?
No. A system’s ability to choose actions or adapt does not establish that its choices are correct, safe, or dependable. Definitions and architecture descriptions explain how a system may operate; they do not validate a particular product. Assess its actual behavior, permissions, safeguards, failure handling, and human escalation separately.
Nor does the phrase “retry loop with a nicer error message” accurately describe every marketed agent. That claim requires evidence about a particular system, such as documented control flow, an observable execution trace, or an implementation showing that actions do not change in response to results. Without that evidence, judge the label by the system’s demonstrated decision authority and adaptation rather than assuming either genuine agency or a mere loop.
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