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Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →If you keep supplying an AI agent with its next move, reset the task around the result you need: define the goal, what counts as done, the relevant context, and the boundaries it must respect. Let the agent choose intermediate steps when the route can reasonably depend on what it finds. This is a practical way to reduce unnecessary steering—not a guarantee that every agent will need fewer prompts.
What should you put in an AI agent prompt?
A goal is more than a broad wish such as “handle this project.” It describes the result you want and gives the agent enough information to tell whether its work meets your needs. OpenAI describes agents as “systems that independently accomplish tasks on your behalf,” and a workflow as steps executed to meet a user goal (OpenAI’s practical guide to building agents). The useful shift is to specify the destination and essential boundaries, rather than scripting every step when the route depends on what the agent discovers.
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For a multi-step task, build a brief from these elements. This is a practical synthesis of OpenAI and Google guidance, not a quoted vendor template.
- Goal: What result should exist at the end?
- Done means: What observable conditions make the result acceptable?
- Context: Which facts, files, audience, or background should the agent use?
- Boundaries: What must it not do? Which actions require your approval first?
- Tools: Which tools or sources may it use, if relevant?
- Uncertainty: What should it do when information is missing or contradictory?
- Deliverable: What should the final output contain, and in what format?
Include the pieces that resolve real ambiguity; not every task needs every item. OpenAI recommends precise instructions, while Google Cloud’s prompt-design guidance covers clear roles where relevant, output formats, edge cases, missing data, and removing irrelevant instructions (Google Cloud’s prompting strategies).
Example: replace step-by-step steering with a goal brief
For a procurement comparison, you could write:
Prepare a two-page comparison of the three proposals in the supplied folder for a nontechnical procurement team. Recommend one using cost, delivery timeline, and support as criteria. Cite each factual comparison to the relevant proposal. Do not contact vendors or make a purchase. If a proposal omits a criterion, mark it unknown. Return a comparison table followed by a short recommendation.
This specifies the audience, deliverable, decision criteria, evidence standard, action limits, missing-data behavior, and format. It does not dictate which proposal to open first or how to perform each intermediate step. Those details can remain flexible unless they affect the result or create a risk.
How much process should you specify?
For a bounded task, keep the instruction focused
If the deliverable and acceptance criteria are clear, start with the goal brief. Adding irrelevant directions can obscure what matters. Google Cloud advises removing irrelevant instructions and splitting requests that bundle too many distinct cognitive actions. If the task contains genuinely separate deliverables, separate them or state the order and relationship between them; do not prescribe a chain of steps merely to make the prompt longer.
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For long-running or tool-using work, add checkpoints
When a task takes multiple stages or uses tools, say what planning and progress reporting you expect. OpenAI’s prompt guidance recommends thorough planning for agentic and long-running tasks, clear preambles for major tool decisions, and organized task tracking (OpenAI’s prompt engineering guide). Specify which actions need a check-in, what evidence to retain, and what the agent should report if it cannot continue. Keep checkpoints proportional: they should help you oversee important decisions, not turn every small move into a permission request.
When should you break up a task or ask a clarifying question?
Keep related work together when one outcome and shared criteria make the pieces coherent. Split it when the request bundles distinct deliverables, decisions, or audiences that are easier to judge separately. Ask for clarification when a missing choice would materially change the result or authorize a consequential action. Otherwise, tell the agent how to handle uncertainty—for example, mark a missing value unknown, state an assumption, or return a short list of unresolved questions instead of inventing an answer.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What a goal brief can—and cannot—prevent
A clear destination does not guarantee correct execution. An agent can misunderstand what you want, make an error, encounter a tool failure, or follow misleading third-party instructions. OpenAI’s Model Spec distinguishes misaligned goals from execution errors and describes mitigations including respecting instruction hierarchy, asking clarifying questions where appropriate, reducing errors, and expressing uncertainty (OpenAI Model Spec, 2025-04-11).
For consequential work, make the verification standard explicit: require supporting evidence for factual claims, ask the agent to flag uncertainty, and keep approval gates around actions such as contacting people, spending money, or changing important data. A short goal alone is not enough when the task needs meaningful constraints or checks. The aim is to stop micromanaging low-risk intermediate steps while staying in control of the outcome and consequential decisions.
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