A clear AI agent briefing spells out the task, goal, context, deliverable, constraints, and authority. Use this free checklist to catch ten common omissions before you delegate; then adapt the template to the task. It is an editorial synthesis of official OpenAI and Anthropic guidance, not a vendor-issued standard or a guarantee of results.
How to use this checklist
Read the ten anti-patterns, then revise your instruction to close only the gaps that matter. For a simple request, a sentence or two may be enough. For work involving tools, multiple steps, sensitive information, or consequential actions, define the boundaries and completion conditions more carefully.
Prompting guidance varies by model and platform. OpenAI advises clearly identifying the task and providing necessary context; Anthropic likewise recommends clear, direct instructions. Neither source establishes that this particular checklist will work identically across agents or guarantee a specific result. See OpenAI’s guidance on creating a good prompt and Anthropic’s prompting best practices.
10 AI agent briefing anti-patterns
1. Giving no clear task or goal
“Do this for me” leaves the agent to guess both the work and what success means. Name the task, then say what the result should accomplish. For example, replace “Look at this report” with “Summarize the report’s three main findings for a project manager deciding whether to extend the pilot.” OpenAI recommends identifying the task clearly and providing context in its prompt guidance; Anthropic recommends explicit instructions in its prompting best practices.
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2. Assuming the agent shares your context
Include background, source material, definitions, and relevant facts the agent cannot safely infer. If it should rely on a particular document or set of links, identify them; if it should not use other sources, say so. Context can provide information outside a model’s training data or restrict it to selected resources, as described in OpenAI’s prompt engineering guide. Anthropic also warns against prompts that assume shared context in its context engineering guidance.
3. Using vague or conflicting directions
Replace subjective terms such as “good,” “quick,” or “professional” with observable requirements where they affect the result. If instructions conflict, state which priority wins—for example, “Keep the answer under 300 words; if a safety caveat is necessary, include it even if that exceeds the limit.” Put ordered work in steps when order or completeness matters. Anthropic recommends direct language, specificity, and sequential instructions in its context engineering guidance and prompting best practices.
4. Failing to define the deliverable
Specify what the agent should return, including format, audience, and any important limits. “Analyze these notes” could produce a narrative, a table, or a list of recommendations. “Return a five-row table with issue, evidence, and recommended owner” makes the expected output easier to judge. Anthropic explicitly recommends specifying output format and constraints in its prompting best practices.
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5. Leaving constraints and priorities implicit
State scope, required inclusions, exclusions, and priority order. Say which files or period to cover, what must be included, and what to leave out. If completeness conflicts with brevity, clarify which matters more. OpenAI’s prompt guidance emphasizes necessary context and focus areas; Anthropic recommends explicit constraints in its prompting best practices.
6. Granting unbounded autonomy
A broad instruction such as “Review my emails and take whatever action is needed” does not distinguish routine work from decisions that require your approval. Define permitted actions and approval points: for example, allow sorting and summarizing messages, but require confirmation before sending, deleting, or sharing anything. OpenAI cautions that wide latitude can expose an agent to harmful instructions in third-party content and recommends asking it to do specific things in Understanding prompt injections. Its safety guidance also recommends review before consequential actions.
7. Leaving tool expectations unclear
If a task depends on browsing, files, code execution, or another tool, explain what the tool is for and when to use it. Clarify which sources or actions are in scope and what to do if a tool fails or returns incomplete information. Anthropic recommends tools with clear purposes and unambiguous inputs in its context engineering guidance. OpenAI’s practical guide to building agents describes using tools to gather context and take actions within guardrails.
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8. Omitting useful examples or task sequence
For complex or repeated work, add a small number of representative examples or give the steps in order. An example can show the desired level of detail, tone, or transformation more clearly than abstract adjectives. Choose examples that illustrate the normal pattern rather than trying to list every edge case. OpenAI documents examples as a prompting technique in its prompt engineering guide; Anthropic recommends diverse canonical examples and ordered steps where they help ensure completeness in its context engineering guidance and prompting best practices.
9. Overloading the briefing with brittle rules
Do not try to script every possible branch or paste a catalogue of unlikely exceptions into the prompt. Excess detail can make the instruction fragile and harder to apply when the situation changes. Keep the rules that define expected behavior, and give the agent a sensible way to handle cases not covered. Anthropic describes brittle over-specification and recommends a minimal set of information that fully outlines expected behavior in its context engineering guidance.
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10. Skipping completion checks and a path for getting unstuck
Tell the agent what counts as done, what it should report if it cannot proceed, and when it must pause for your input. A useful completion check might require a specified file, a list of unresolved items, or evidence that all requested sources were reviewed. For agent workflows, OpenAI’s practical guide discusses recognizing completion, correcting actions, halting, and returning control when needed. OpenAI also recommends reviewing important actions in its prompt-injection safety guidance. If the output misses the mark, refine the instruction iteratively rather than assuming one prompt will fit every task.
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A reusable task briefing template
Copy and adapt the fields that matter. Leave out sections that are irrelevant; add detail when the task’s complexity or risk warrants it.
Task: [The specific work to complete.]
Goal: [What a successful result enables or answers.]
Context and sources: [Relevant facts, files, links, or trusted materials.]
Deliverable: [Format, audience, length, and required contents.]
Constraints and priorities: [What to include, what to avoid, and which requirements take precedence.]
Tools and authority: [Tools to use, permitted actions, and actions requiring confirmation.]
Completion check: [How to tell the work is complete and what to report if blocked.]
Example: [Optional representative input/output when it clarifies the expected pattern.]
For a multi-stage workflow, consider splitting the request into focused steps rather than giving the agent an unwieldy instruction. OpenAI’s prompt engineering guidance and Anthropic’s context engineering guidance support providing relevant context and right-sizing instructions.
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Set safety boundaries beyond the prompt
A well-written briefing does not by itself make an agent safe. Third-party material—such as email or web content—can contain malicious instructions. Limit access to what the task needs, specify which content is information to process rather than authority to follow, and require review before consequential actions. OpenAI explains these risks and safeguards in Understanding prompt injections.
Also consider whether an agent is appropriate for the workflow at all. OpenAI’s practical guide to building agents recommends validating the fit, particularly for workflows involving contextual decisions, difficult-to-maintain rules, or unstructured information. A clear brief can improve the handoff; it cannot remove the need for suitable permissions, oversight, and judgment.
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