Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsA retry loop is not proof that an AI system is agentic. A loop is an execution pattern: a model may receive a result, call a tool, and then run again. What matters is whether the model can choose or revise its actions in response to what it observes—or whether code simply repeats a prescribed operation.
What makes an AI agent agentic?
The useful test is who controls the next step. Anthropic defines an agent as an AI model that directs its own processes and tool use to accomplish a task, rather than following a fixed script. In that framing, an agent can plan, act, observe the result, adjust and continue until the task is complete or it needs human input.
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OpenAI’s Agents SDK describes an agent as an LLM configured with instructions, tools and optional runtime behavior. Its runner calls the model, executes tool calls or handoffs, and continues until a stopping point. That cycle is ordinary agent infrastructure; its existence alone does not show that the model is making meaningful decisions inside it. OpenAI Agents SDK documentation
Google’s glossary offers another useful description: an agentic loop moves through Observe, Reason, Act and Feedback. These definitions are engineering perspectives, not a universal certification standard. Taken together, they point to three important properties: a goal, some authority to select or change actions, and feedback that can affect what happens next.
Is an AI agent just a loop?
No. A loop can host agentic behavior, but repeating a step does not make the step self-directed. A program might retry the same request after an error, or follow a fixed sequence of model calls and tools. The system may be useful, but the loop itself does not establish that the model chose a new strategy.
OpenAI’s practical guide draws a related boundary: applications that use an LLM but do not let it control workflow execution—such as simple chatbots, single-turn LLM calls or sentiment classifiers—are not agents under the guide’s framing. The distinction is not whether a model appears somewhere in the software; it is whether the model has a role in choosing how the work proceeds.
Agent, fixed workflow or hybrid?
Compare systems by the decisions they allow, rather than treating “agent” and “workflow” as mutually exclusive labels. A workflow can contain model-directed choices, and an agent can operate inside a tightly controlled workflow. Google’s architecture guidance discusses both agent and workflow patterns. Google Cloud’s design-pattern guidance
| Question | Fixed workflow | Model-directed agent | Hybrid design |
|---|---|---|---|
| Who chooses the next action? | Code or a predefined sequence determines it. | The model can select among available actions or tools. | Code sets boundaries; the model chooses within them. |
| Can feedback change the plan? | Usually only through predefined branches or retry rules. | The model can use observations to alter its next step. | The model may adapt, while orchestration constrains which changes are allowed. |
| How is the goal represented? | As prescribed steps, conditions or outputs. | As a task outcome that guides choices across steps. | As a model-directed task bounded by application rules. |
| How does the process stop or escalate? | At a programmed endpoint, limit or branch. | At completion, a failure condition, a limit or a request for human input. | Through model decisions and explicit runtime or application limits. |
| What is the main trade-off? | More predictable and constrained behavior, with less flexibility. | More flexibility, with decisions that may be less predictable. | A balance between flexible choices and controlled execution. |
How to tell whether an agent can adapt
Ask for a trace of a real task, not just a diagram of a loop. A useful trace shows the initial goal, the available tools, each action, the result returned by the environment, and the reason for the next action. The central question is whether an observation changed the model’s choice.
- Action authority: Can the model select among tools or actions, or does code prescribe the next one?
- Response to results: After receiving an unexpected result, can it change its plan, or does it repeat the same request under the same conditions?
- Goal and success condition: Is there an outcome guiding the next step, rather than merely a sequence to execute?
- Stopping and escalation: What ends the run—completion, a failure condition, a budget or other limit, or a request for human input?
- Division of responsibility: Which choices belong to the model, and which are made by deterministic orchestration?
A product can pass some of these checks and not others. For example, it may choose a tool but be unable to revise its plan after a result. Describing exactly which decisions are model-directed is more informative than applying an all-or-nothing label.
The loop includes more than the model
In a deployed system, the model is only one part of the arrangement. The harness or orchestration layer submits instructions and handles model responses; tools run in an environment; and application logic and permissions constrain what can happen. OpenAI’s architecture guide separates these roles, which helps explain why a model’s capabilities alone do not describe the behavior of the whole system. OpenAI’s Agents API architecture guide
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Runtime controls can also bound a cycle. The OpenAI Agents SDK for JavaScript documents ways to stop on an initial tool result and prevent repeated tool calls. Such controls help govern execution; they do not, by themselves, demonstrate planning or adaptation. OpenAI Agents SDK for JavaScript documentation
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What the agent label does—and does not—tell you
Calling a system an agent does not establish that it is correct, safe or reliable. The architecture definitions describe how control and tool use may work; they are not evidence that a particular product performs well. To judge a specific system, look for documented control flow or a reproducible trace showing whether its actions change with the results it receives, then evaluate its permissions, limits and human oversight separately.
There is no validated numerical threshold in these definitions for when a system becomes agentic. The practical distinction is qualitative: a model-directed cycle can choose or revise actions toward a goal, while a fixed retry loop repeats behavior prescribed elsewhere. Some systems sit between those poles, with deterministic orchestration bounding model choices.
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