An always-on AI agent has a runtime kept available to receive work or respond when needed; a scheduled automation starts when a clock rule fires. Those terms describe different things: runtime availability and the trigger that starts a task. Either kind of trigger can launch an agent, while an always-available service can handle scheduled or event-driven work. The key question is whether the system follows fixed steps or can choose and adapt its actions to reach a goal.
What makes an AI agent different from an automation?
An automation follows a predefined process: when a known condition occurs, it carries out specified steps. An agent has more discretion over how it completes a task. Anthropic defines an agent as “an AI model that directs its own processes and tool use when accomplishing a task—that is, deciding for itself how to achieve what users want, rather than following a fixed script.” In practice, an agent may plan, use a tool, observe the result, and adjust its next step until it finishes or needs human input. Anthropic’s article on trustworthy agents and OpenAI’s guide to building agents describe this distinction.
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An LLM can also be used inside an ordinary fixed workflow without making the whole process an agent. For example, a workflow might always collect a message, ask a model to classify it, then route it according to a fixed rule. The interpretation step is flexible, but the overall sequence remains predefined.
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What does “always-on” mean?
“Always-on” describes a deployment or runtime that remains available to receive work. It does not, by itself, mean a model is reasoning or making API calls every moment. A continuously available service might wait for a request, event, or queued job before doing anything; it might also run an internal loop for a task that needs ongoing attention.
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Google Cloud distinguishes dedicated stateful instances for always-on loops from background worker pools that consume queued messages and jobs that run to completion. These are runtime choices, not proof of continuous model inference. Its Cloud Run guidance for AI agents describes these patterns alongside request-driven services.
How do schedules and event triggers fit in?
A schedule determines when work starts: for example, at a specified time, on a recurrence, or according to a cron expression. It can start a fixed workflow or an agent session. Anthropic says each firing of a scheduled Claude Managed Agent deployment starts a new session; scheduling does not make that session continuously active. Its June 9, 2026 announcement described scheduled deployments as a public beta, with pause, resume, archive, and on-demand trigger options.
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An event trigger starts work when a supported event occurs rather than waiting for the next scheduled interval. ChatGPT tasks, for example, can respond to supported activity in Gmail, Slack, or GitHub, subject to account eligibility and workspace controls. The product’s available triggers, plan eligibility, active-task limits, and other controls can change; consult OpenAI’s current scheduled-tasks help page for the latest details.
Compare the four common designs
| Design | What starts work | How steps are chosen | Typical fit and trade-off |
|---|---|---|---|
| Scheduled automation | A clock, recurrence, or cron rule | Predetermined instructions or workflow; it can also start an agent | Useful for reports, syncs, reminders, and repeated checks. Predictable and simple, but it may run when nothing has changed or miss an unexpected event. |
| Event-triggered automation | A supported event, such as a new message or repository activity | A predetermined workflow or an agent, depending on the design | Useful for reacting to incoming work. It can respond sooner than a periodic batch, but depends on integrations and permissions. |
| Always-available agent runtime | An incoming request, event, queued work, or an internal loop | The agent can choose tools and adjust steps toward a goal | Useful for long-running tasks or systems that need to be ready for work. It requires runtime, state, monitoring, and controls; availability does not necessarily mean continuous model calls. |
| LLM-assisted fixed workflow | A schedule, event, or request | The workflow stays fixed while the model handles a bounded interpretation step | Useful when a predictable process needs a classification or summary step. It retains a defined process, though the model’s interpretation can vary. |
This is a comparison of design patterns, not a benchmark. OpenAI notes that fixed workflows can be easier to audit but less flexible, while agents can adapt their approach. Google Cloud’s deployment options show that runtime pattern is a separate decision from how much discretion the model has.
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Which design should you choose?
- Choose a conventional automation when the conditions and actions are stable and the process needs to be easy to audit.
- Add a bounded LLM step when the workflow is predictable but one step needs interpretation, such as classifying a request or summarizing an attachment.
- Choose an agent when the system needs to select among tools, respond to changing conditions, or determine a sequence of actions from a goal.
- Choose the trigger separately: use a schedule for work due at known intervals, an event trigger for work that should start after a known event, and an available runtime when the system must be ready without waiting for a periodic batch.
For implementation, OpenAI frames runtime choice as a decision between a managed long-running agent, an SDK where the developer controls the loop, and direct model/API orchestration. The right option depends on how much control and infrastructure responsibility the application requires; see OpenAI’s Agents documentation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What permissions and oversight should you plan for?
More adaptive behavior can mean more consequential choices, particularly when an agent can change external data. Give it only the access its task requires, set action limits, and require confirmation where mistakes would be costly. OpenAI’s agent-building guidance discusses safety controls, while its scheduled-task documentation explains that connected-app permissions govern what a task can read or change and that actions requiring approval may pause for review. OpenAI agent safety guidance and ChatGPT task permissions and controls cover these considerations.
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