An AI agent prompt is a set of instructions that guides an AI agent’s role, task, workflow, and response. To write one, define the goal and success criteria, provide relevant context, spell out actions and edge cases, clarify tool boundaries, specify the output, then test and revise it. A prompt guides an agent; it does not give it tools or capabilities that its configured model and environment lack.
What an AI agent prompt does
An agent prompt tells an AI system what it is responsible for, how it should approach a task, and what kind of result to return. In practice, those instructions work alongside the configured model and the tools made available to it. OpenAI’s Agents SDK documentation describes an agent as configured with instructions, a model, and tools; the prompt is only one part of that setup.
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For example, a support agent might be instructed to identify a customer’s issue, retrieve the relevant account record, summarize the applicable policy, and escalate cases that need a human. The prompt can describe that workflow, but the system must also have access to the account lookup and escalation tools for those actions to be possible.
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1. Define the job and success condition
State what the agent must accomplish, who the result is for, and what counts as a satisfactory outcome. Include background information or source material that affects the answer. OpenAI’s prompting advice recommends being clear about the task, supplying context, and specifying tone or style when it matters.
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“Help with customer questions” is too broad to guide a multi-step workflow. A more useful goal might be: “Answer questions about order status by checking the order record, reporting the latest available status, and directing refund requests to a human support representative.”
2. Separate stable instructions from task-specific input
Keep reusable guidance—such as the agent’s role, boundaries, and general response rules—distinct from the details of an individual request. OpenAI’s API prompting guidance distinguishes system-level role or tone guidance from task-specific details and examples supplied with a request. This separation makes the prompt easier to review and update without mixing permanent rules with changing case information.
3. Turn the workflow into actions
For a task involving several steps, write the expected actions in order. Make each step concrete: ask for a missing identifier, retrieve a record, compare it with a policy, summarize the result, or hand off the case. OpenAI’s A practical guide to building agents recommends clear, action-oriented instructions, task decomposition, and guidance for incomplete inputs and unexpected situations.
Include branches for common exceptions. For example, say what to do if the user has not provided an order number, the lookup returns no match, or the user asks for something outside the agent’s scope. Make it clear whether the agent should ask a question, try an approved retrieval step, stop, or escalate.
4. Set tool boundaries
Name the tools relevant to the workflow and explain when the agent should use them. Instructions cannot create access to a tool: the system must have the necessary function or API configured. The Agents SDK tools documentation describes tools as functions or APIs an agent can invoke.
Distinguish information retrieval from actions that change something. Looking up a policy is different from issuing a refund or sending a message. Specify which actions are allowed, which require confirmation, and which must be handed to a person. This boundary is especially important when an action is sensitive, difficult to reverse, or high stakes.
5. Specify the response format
Tell the agent what the final answer should contain and how it should be structured. For a human-facing response, specify the necessary information and any relevant tone. For a downstream system, state the exact fields or format it needs. Clear output requirements reduce ambiguity; they do not guarantee that every response will comply, so test the format as part of evaluation.
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6. Use examples where they clarify a pattern
A small number of representative input-and-output examples can show how to handle cases that are hard to express as a short rule. Use examples that illustrate meaningful variation—such as a complete request, missing information, and an out-of-scope request—rather than several versions of the same easy case. OpenAI’s prompt engineering guidance covers examples as a way to steer model behavior.
7. Test, evaluate, and revise
Try the prompt on representative cases before using it in a real workflow. Include ordinary requests, incomplete inputs, unexpected questions, and cases where the agent should stop or escalate. After changing the prompt, check whether the behavior improved across those cases rather than judging it from a single response.
OpenAI’s prompting guidance recommends evaluation cases and managing production prompts in versioned, code-managed helpers. Anthropic’s prompting best practices also cautions that techniques measured on one model should be checked against evaluations before being applied to another. Prompt instructions guide behavior; they do not make output deterministic.
A reusable prompt outline
This is an adaptable drafting aid, not a universal vendor-mandated format. Include only the sections that fit the task.
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Goal: The task and what a successful result contains
Context: Relevant facts, policies, or source material
Tools: Available tools and when to use each
Workflow:
1. First action
2. Next action
3. Required check or handoff
If information is missing: Ask, retrieve, or stop
If the request is outside scope: Respond safely or escalate
Output: Format, audience, and required fields
Examples: Representative input/output pairs, if usefulCommon prompt-writing trade-offs
Specificity versus flexibility
Explicit steps make the expected behavior easier to understand, but overly rigid instructions can fail on legitimate variations. Describe common branches and define when the agent should ask for clarification or hand off instead of trying to prescribe every possible conversation.
Useful context versus too much context
Provide information that changes how the agent should answer or act. Models have finite context windows, so irrelevant background can crowd out the material needed for the current task. OpenAI’s prompt engineering documentation discusses context and model context limits.
One agent versus a more complex setup
Start by making one agent effective at its job. OpenAI’s practical guide recommends maximizing a single agent’s capability first and adding complexity when the workflow calls for it. Adding agents, tools, and orchestration can increase what a system handles, but also makes its behavior and maintenance more involved.
Autonomy versus human oversight
For sensitive, irreversible, or high-stakes actions, keep human review in the workflow until the system’s reliability is established. A prompt can define when to pause and escalate, but the surrounding application must enforce any required permissions or approval steps.
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What a prompt cannot do
- It cannot give the agent access to an API, database, or other tool that has not been configured.
- It cannot guarantee identical outputs for every run or eliminate the need for evaluation.
- It cannot substitute for application-level controls over permissions, approvals, and actions with side effects.
- It cannot make guidance written for one model automatically effective on another; test the prompt with the model and workflow you plan to use.
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