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What makes an AI agent “always-on”?
“Always-on” describes how a system is deployed: its runtime is kept available to receive requests, events, or queued work. It does not mean the AI model is reasoning or generating output every moment. Google Cloud, for example, distinguishes dedicated stateful instances from background worker pools and run-to-completion jobs; these are different runtime patterns, not evidence of continuous model inference. Google Cloud’s Cloud Run documentation describes these deployment choices.
An agent, meanwhile, is defined by how it pursues 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 can plan, take an action with a tool, observe the result, and adjust its next step until it finishes or needs human input. Anthropic’s article, Trustworthy agents in practice (April 9, 2026), sets out this definition. OpenAI likewise contrasts predefined workflow steps with agents that plan, choose tools, and adapt their approach. OpenAI’s practical guide to building AI agents explains that distinction.
How do always-on agents differ from scheduled automations?
A schedule answers “when should work start?” Agent autonomy answers “how should the work be carried out?” A scheduled job may follow a fixed checklist or start an agent session; keeping a runtime available does not itself make its work agentic.
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| Design | What starts work | How steps are selected | Typical fit | Main tradeoff |
|---|---|---|---|---|
| Scheduled automation | A clock, recurrence, or cron rule | Predetermined instructions or workflow; it can also invoke an agent | 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 | Responding to incoming work | Can react without waiting for the next interval, but depends on integrations and permissions. |
| Always-available agent runtime | An incoming request, event, queued task, or internal loop | The agent can choose tools and adjust steps toward a goal | Long-running tasks or systems that must remain ready | Needs runtime, state, monitoring, and controls; availability does not imply continuous model calls. |
| LLM-assisted fixed workflow | A schedule, event, or request | The workflow remains fixed; a model handles a bounded interpretation step | A predictable process with one ambiguous classification or summary step | Adds flexible interpretation without delegating the whole process, but model output can vary. |
This comparison synthesizes the cited descriptions; it is not a performance benchmark. OpenAI notes that fixed workflows are more auditable but less flexible, while agents can adapt. Google Cloud distinguishes services, instances, worker pools, and jobs by runtime pattern. OpenAI’s guide and Google Cloud’s documentation provide the underlying distinctions.
When should you use a schedule, an event trigger, or an agent?
- Use ordinary automation when conditions and actions are stable and the process should be easy to audit.
- Add a bounded LLM step when the process is still predictable but one step requires interpretation, such as classifying a request or summarizing an attachment.
- Use an agent when the system must choose among tools, handle changing conditions, or determine a sequence of actions from a goal.
- Use a schedule for work that belongs at known intervals; use an event trigger when a known external event should start it; keep a runtime available when it must be ready without waiting for a periodic batch.
- Set permissions and approval gates according to the consequences of the actions. Connected-app permissions determine what a task can read or change, and some actions can pause for approval. OpenAI’s scheduled tasks help page describes task and app controls.
How do current products implement these options?
ChatGPT scheduled tasks
ChatGPT scheduled tasks support one-time and recurring work, monitoring, and supported app-event triggers. The help documentation describes triggers for activity in Gmail, Slack, and GitHub, subject to account eligibility and workspace admin controls. Plan eligibility, active-task limits, scheduling options, and app access can vary, so consult the current ChatGPT scheduled tasks documentation for the account and workspace in question.
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Claude Managed Agents scheduled deployments
Anthropic announced scheduled deployments for Claude Managed Agents as a public beta on June 9, 2026. Each cron firing starts a new session; deployments can be paused, resumed, archived, or triggered on demand. This is one vendor’s product implementation, and its beta status or behavior may change. See Anthropic’s Claude Managed Agents documentation for current details.
Google Cloud runtime choices
Google Cloud describes Cloud Run services for stateless, request-driven agents; instances for dedicated stateful always-on loops; worker pools for background distributed agents consuming message queues; and jobs for run-to-completion work such as batch or scheduled sync tasks. Its documentation lists Agent Development Kit, Dify, LangGraph, and n8n as example frameworks, not universal recommendations. See Google Cloud’s Cloud Run overview.
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OpenAI agent runtime choices
OpenAI’s Agents documentation frames runtime selection as a choice among a managed long-running agent, an SDK where the developer controls the loop, and direct model/API orchestration. The appropriate option depends on how much of the runtime and orchestration the developer wants to manage. OpenAI’s Agents guide outlines these approaches.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What controls should you put around an agent?
Giving a system discretion over tools can let it affect external data, so match its authority to the task. Specify what it may read or change, limit available actions, and require confirmation for consequential operations. A workflow can preserve human review at the steps where mistakes would matter most; agent autonomy does not remove the need for oversight.
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