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An AI agent prompt is the set of instructions that guides an agent’s role, behavior, workflow, and response. To write one, define the job and success condition, provide relevant context, specify the steps and tool boundaries, explain what to do when information is missing, and state the required output. A prompt guides an agent; it does not give it tools or capabilities that its configured model and system do not have.
What is an AI agent prompt?
An AI agent prompt is the instruction set that tells an agent what it is responsible for, how to approach a task, and what result to return. In a working agent, the prompt is only one part of the setup: the model and available tools also shape what the system can do. The OpenAI Agents SDK describes an agent as configured with instructions, a model, and tools (OpenAI Agents SDK: Agents).
That distinction matters in practice. Writing “check the customer’s order status” cannot make an agent access an order database. The system must actually provide a suitable tool, and the instructions should say when and how to use it.
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- Role and scope: What the agent is responsible for, and what falls outside its remit.
- Goal and success condition: The task to complete and the information or action that counts as a satisfactory result.
- Context: Relevant facts, policies, or source material that affect the answer.
- Workflow: The actions to take, in order, including important decision points.
- Tool boundaries: Which available tools to use and under what conditions.
- Exceptions: What to do if information is missing, a step fails, or the request is outside scope.
- Output requirements: The format, audience, and required fields for the response.
Examples can help clarify a pattern, especially when the task has several valid input or output forms. Include representative examples rather than repeating near-identical cases. For a machine-readable response, specify the expected structure explicitly.
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How to write an AI agent prompt
1. Define the task and what success looks like
Describe what the agent must accomplish, who the result is for, and what a complete answer or action contains. Include only background that can change the result. OpenAI’s prompt guidance recommends stating the task clearly, supplying context, and specifying a preferred tone or style when it matters (OpenAI Help Center: How do I create a good prompt for an AI model?).
For example, “Help with order questions” leaves the job open to interpretation. A more useful goal is: “For a customer asking about an order, provide its current status and estimated delivery date, or explain what information is needed to look it up.”
2. Separate standing instructions from the task’s input
Keep reusable role, scope, and behavior guidance separate from the details of each individual request. In OpenAI API prompting guidance, overall role or tone guidance belongs in the system message, while task-specific details and examples can go in user messages. The exact message structure depends on the platform. Keep repeated instruction and example blocks concise enough for people maintaining them to review (OpenAI API: Prompting).
3. Turn the workflow into actions
For a task with multiple stages, write the stages as explicit actions or outputs. Instead of “resolve the issue,” specify whether the agent should identify the issue, retrieve a relevant record, summarize what it found, and then answer or hand the case to a person. State the conditions that change the next step, such as an absent account identifier or a record that cannot be found.
OpenAI’s practical guide to building agents recommends clear instructions and breaking tasks into smaller steps. It notes that this can reduce ambiguity and support smoother workflow execution (OpenAI: A practical guide to building agents).
4. Set tool boundaries and exception handling
Name the tools the agent can actually use and say when each is appropriate. A retrieval tool may fetch a record; a separate action tool may update a system or send a message. Instructions should not imply that the agent may take an action simply because the prompt mentions it. Define what to do if a tool is unavailable, returns incomplete information, or produces an unexpected result: retry only when appropriate, ask for the missing information, report the limitation, or hand off the case.
For actions with sensitive, irreversible, or high-stakes consequences, specify when a human must review or approve the action. OpenAI’s agent-building guide recommends human oversight for such actions until reliability has been established.
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5. Specify the output
Tell the agent whether to answer in plain language, return named fields, or use another defined format. If the result will be consumed by software, make the format requirements explicit and decide what the agent should return when a value is unknown. Avoid instructions that demand both a fixed structure and an incompatible free-form response.
6. Test and revise
Try the prompt against representative cases: ordinary requests, missing information, unexpected inputs, tool failures, and requests outside scope. Check whether the output meets the success condition, then revise and evaluate again. OpenAI’s API prompting guidance recommends prompt testing and evaluation cases, and describes managing production prompts in version-controlled code. Prompt wording is not a guarantee of deterministic output; measure the behavior you need in the system where you plan to use it (OpenAI API: Prompting).
A reusable AI agent prompt template
This is a practical starting point, not a universal format required by any one vendor:
Role: The agent’s responsibility and scope
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 usefulAdapt the sections to the platform and task. A simple agent may not need examples or several workflow steps; a tool-using agent handling exceptions may need both.
Common prompt-writing trade-offs
Specificity versus flexibility
Explicit steps make expectations clearer, but rules that are too rigid can fail on legitimate variations. Describe common branches and identify when the agent should ask a clarifying question or hand off rather than guessing.
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Context versus focus
Relevant context can ground an answer, but models have finite context windows. Include information that changes the task or its result, and keep reusable instruction blocks manageable. OpenAI’s prompt-engineering documentation explains the role of context and context-window limits (OpenAI API: Prompt engineering).
One agent versus multiple agents
Start by giving one agent the tools and instructions needed for its workflow. OpenAI’s practical guide recommends maximizing a single agent’s capability first; orchestration with multiple agents may make sense when the workflow calls for it, but it also adds design and evaluation considerations.
Porting prompts between models
Do not assume a prompt that works well on one model will behave the same way on another. Anthropic’s prompting guidance distinguishes model-specific techniques from more general practices and recommends checking techniques against evaluations on the model being used (Anthropic: Prompting best practices).
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