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Give an AI agent one bounded outcome to own—not necessarily one tiny action. Define what it must produce, what it may use, when it starts, and when it must stop or hand work back. Start with one agent for a manageable workflow; split the work only when it contains genuinely distinct responsibilities. Use a persistent workspace when the job needs files, commands, artifacts, or resumable state.
What “one job” means for an AI agent
Single responsibility is about a clear result, not an artificially small task. An agent can perform several steps and still have one job if those steps serve the same bounded outcome. For example, an agent might inspect a set of project files, run an approved check, and produce a concise report. Its responsibility is the report-producing workflow; unrelated work should remain out of scope.
OpenAI Academy recommends defining what an agent is responsible for, when it begins, what makes it pause or stop, what tools and information it can use, and what process and rules it must follow. See OpenAI Academy’s guide to workspace agents.
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Specify the job before choosing the architecture
- Outcome: What useful result must exist when the job is done?
- Trigger: Does it begin on a schedule, an event, or a user request?
- Process: What steps or checks should it follow?
- Tools and information: Which connected apps, data, and capabilities are necessary?
- Boundaries: What must it not do, and what requires human approval?
- Stop or handoff: When is the work complete, blocked, or ready for human review?
When an agent is a good fit
Agents are most useful when work recurs, has a recognizable output, starts from a time or event trigger, or requires tools and connected systems. A task with clear steps but a bounded outcome may still fit a single agent. By contrast, open-ended one-off exploration may be better handled in ordinary chat, and a predictable task that fits in one model call may be simpler and less costly without an agent. Google Cloud’s guide to agentic AI design patterns discusses selecting patterns based on task requirements, performance, cost, and human involvement.
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Signals that the task may be over-scoped
- The agent is expected to own unrelated outcomes, such as both making a decision and carrying out a separate operational process.
- There is no clear trigger or definition of completion.
- It needs broad access “just in case,” rather than a specific set of tools for the stated outcome.
- Its work cannot be evaluated without separately judging several different responsibilities.
These are reasons to clarify the job, not automatic proof that multiple agents are needed. First see whether one well-defined workflow can produce the desired result.
One agent or several?
For a manageable task, begin with one agent and refine its instructions, core logic, and tool definitions. Google Cloud’s Architecture Center puts it plainly: “If you’re early in your agent development, we recommend that you start with a single agent.” More tools and complexity can make tool selection less reliable, increase latency, or leave work incomplete. A multi-agent design is warranted when responsibilities are genuinely distinct and the benefit of specialization outweighs the added coordination and operational burden.
| Pattern | Best fit | What to weigh |
|---|---|---|
| Single agent | A multi-step workflow with one clear outcome and responsibilities that belong together. | Simpler to refine as a starting point; still requires suitable tools, instructions, evaluation, and review. |
| Specialized agents | A larger objective that can be divided into distinct responsibilities or subtasks. | Coordination, orchestration, access control, evaluation, and compute cost add complexity. |
| Sequential pattern | A predefined, repeatable sequence in which one step follows another. | Useful when the process is fixed; consider whether agent flexibility is actually needed. |
| Parallel pattern | Independent subtasks that can proceed concurrently. | Requires coordination of outputs and does not help when subtasks depend on each other. |
Choose based on how distinct the responsibilities are, whether work can run independently, the need for files or persistence, flexibility versus a fixed sequence, latency and runtime cost, access-control and evaluation effort, and how much human review is needed. Splitting a workflow merely because it has several steps can add overhead without making the result better.
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A workspace or sandbox is useful when the job depends on inspecting or changing a document directory, running commands, creating artifacts, or resuming after human review in the same environment. These needs involve state beyond a short conversational response. OpenAI’s Sandbox Agents documentation describes workspace capabilities for agent tasks; the appropriate setup depends on the work and the available environment.
If the task only needs to return a short answer and does not need persistent state, a dedicated workspace may be unnecessary. Match the environment to the task rather than treating a workspace as a requirement for every agent.
Give the agent only the access its job needs
Instructions describe what an agent should do; they do not independently grant access to apps or data. Configure the required tools and permissions deliberately, and keep them aligned with the intended work. Which apps and features are available can depend on workspace availability and user permissions. OpenAI’s ChatGPT Workspace Agents help page covers workspace agents for Enterprise and Business.
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Test the scope before relying on it
- Write the job definition: State the outcome, trigger, process, allowed tools and information, boundaries, and stop or handoff conditions.
- Choose the simplest workable pattern: Use one agent for a manageable responsibility; add specialists only for distinct work that merits coordination.
- Choose the environment: Provide a persistent workspace if the workflow needs files, commands, artifacts, or resumable state.
- Configure access: Enable only the apps and tools the job requires, with permissions appropriate to its actions.
- Preview with sample prompts: Check whether the agent stays within scope, uses tools appropriately, produces the intended output, and pauses when expected.
- Inspect and refine: Adjust instructions or configuration based on results. Revisit the architecture if workload needs change.
Workspace-agent availability, app access, and features can depend on the product configuration and user permissions; consult the current OpenAI Help Center details for the relevant Enterprise or Business workspace.
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