Prepare These 5 Assets Before Your AI Agents Take On More Work
TL;DR: , the work itself needs to be clearly defined. That means knowing what the task is, giving AI the right business context, explaining what good work looks like, and deciding when AI can move ahead on its own and when it should ask for human judgment. This article walks through five reusable assets that help teams build workflows AI can support consistently and with confidence.
In my last article, Redesign Work Before You Add More AI Agents, I argued that companies should start by redesigning the workflow before rolling out more AI tools and agents.
You may wonder, if creating more agents is not the first step, where should we start? If a team wants to move beyond individual productivity, isolated pilots, and scattered demos, and make AI part of its product lifecycle and daily operations, what should happen first?
The more AI workflow enablement projects I work on, the more convinced I become that the missing piece is preparing the workflow itself.
Before AI can reliably perform any recurring work, someone has to define what the work is, why it exists, which information is important, what a successful result looks like, and where AI must stop and ask for human judgment.
The reality today is that this preparation rarely gets documented. Most teams jump straight to prompts and agents. Many believe that giving teams access to advanced models will produce better results. Here is the counterintuitive part: the better the model performs, the more costly those missing definitions can become.
The difference is how you ask. A chat interaction begins with a request: “Analyze this.” “Make a presentation.” “Summarize these files.”
An operational workflow begins with a well-defined job. What outcome is required? Which sources are authoritative? What decisions can AI make on its own? What does a passing output look like? When should AI stop and ask for help?
In this article, I want to show what you can do before handing recurring work to an AI assistant or agent. For your team, the work can start with these five reusable assets. Instead of continuing to collect better prompts, create these assets, improve them through practical use, and carry them across the AI models and tools you adopt over time.
1. The Repeated Work Asset
Find the tasks that happen regularly, take meaningful time, follow repeatable steps, use the same types of inputs, or carry enough value or risk to justify a reusable AI workflow.
It can be a weekly report, a monthly business review, a customer proposal, a contract review, a product launch package, or a quarterly planning process.
You need a simple inventory of how you and your team spend your time, which work repeats, and where a reusable process could help. The repeated work list helps you choose tasks based on frequency, effort, risk, and value.
You are my workflow organization assistant.
Based on the work description below, identify the recurring tasks that are most suitable for AI.
Requirements:
1. Include tasks that:
- Repeat regularly
- Follow a consistent process
- Consume meaningful time
- Carry a high risk of avoidable errors
- Or depend on repeated analysis, drafting, review, or coordination
2. For each task, state:
- The specific action
- How often it happens
- Required input
- Expected output
- Evaluation standard
- Time currently required
- Main source of difficulty or error
3. Classify each task as:
- Better suited for a one-time AI conversation
- Better suited for a reusable workflow or agent
- Better kept primarily human-led
4. Explain why you selected each classification.
5. Avoid general advice. Use specific actions.
My work description:
[Paste your description here]2. The Task Asset
Most people provide the topic and leave the rest to AI. They ask AI to “analyze the data,” “prepare the presentation,” or “summarize the documents.” The model then has to infer the audience, decision, format, source priority, level of detail, and quality threshold.
AI often fills in missing information on its own. When those assumptions are wrong, the output can sound confident while still pointing in the wrong direction.
A task asset helps remove these hidden assumptions. It turns a vague request into an assignment AI can execute. Define the objective, the audience, the materials, the constraints, the steps, what good looks like, and when the system should stop and ask. It also gives you something concrete to review before giving AI more responsibility.
Convert the vague request below into a task package that AI can execute.
Output:
- Objective
- Business purpose
- Audience
- Decision or action this work should support
- Materials to use
- Authoritative sources
- Reference-only sources
- Constraints
- Execution steps
- Required output format
- What a good result looks like
- Acceptance criteria
- Risks I need to confirm
- Information that is still missing
- When you must stop and ask me
My request:
[Paste your request here]3. The Context Asset
Context helps AI see what is important. Teams can reorganize, priorities can change, results get updated, policies are rewritten, and the information you relied on six months ago may go out of date. A useful context tells AI what it knows and what it still needs to verify.
Stop re-explaining your work in every conversation. One short document can cover who you are, what you are working on, which sources you can trust, how you make decisions, and what kind of output you never want.
Useful context should not become a kitchen sink for everything that has ever happened. Keep it brief and current. Do not dump months of old chat history into the context, because outdated details and unrelated conversations can bury the valuable information.
Create a concise project context document for AI.
I will reuse it in ongoing AI tasks so I do not need to explain the same context each time.
Include:
1. Who I am
2. What I am currently working on
3. My current objective
4. My target audience
5. The decisions I am trying to support
6. How I usually work
7. The tools and materials I commonly use
8. My preferred output style
9. The types of output I dislike
10. Things I cannot say, publish, share, or do
11. Which sources are trustworthy
12. Which sources are for reference only
13. Important definitions or business rules
14. Facts that may expire or change
15. Information that must be confirmed before use
16. The date this document was last updated
Keep it short, precise, and relevant.
Separate stable information from temporary information.
Flag anything that may need to be updated within the next 30, 60, or 90 days.
My background:
[Paste your information here]4. The Acceptance Test Asset
You need to know what failure looks like before AI output goes to a customer, a production system, or the public. Test the AI agent against existing examples before using it for recurring work. Use examples from your own work, including those you accepted and rejected.
Acceptance tests turn your expectations into something you can check. They show both you and AI what a good result looks like. Examples you accepted and rejected make it easier to tell the difference between output that sounds confident but is wrong and output you can use.
I want to assign this task to AI on a recurring basis.
Create an acceptance-test set for it.
Task:
[Describe the task]
Accepted examples:
[Paste one or more outputs I approved and explain why]
Rejected examples:
[Paste one or more outputs I rejected and explain why]
Use these examples to identify the quality standard.
Do not invent a quality standard that is unsupported by the examples.
Identify any standard that I still need to define.
Provide:
1. Five test examples
2. The passing criteria for each example
3. The evidence required to confirm that each case passed
4. Common errors
5. How to detect fabrication or unsupported conclusions
6. How to detect use of outdated or unauthorized sources
7. Situations that must be given to a person for judgment
8. Any unresolved quality standard that requires my decision
Include:
- A normal case
- A missing-information case
- A conflicting-information case
- A difficult edge case
- A case requiring human judgment5. The Permission Asset
A human-agent system works best when everyone knows where the lines are: what AI can do on its own, what it can prepare for your approval, and what it should never do alone. AI can handle the manual repeated work, you approve the important recommendations, and there is a record of how the final result was produced.
It is also critical for irreversible actions. Deleting a file, modifying a production system, approving a purchase, or publishing something publicly can create consequences that are difficult to reverse.
This asset can become your personal human-agent system. It defines what the agent can handle, what still needs your approval, which data it can use, and where you stay in charge. It also keeps a record of the assumptions it made, what it changed, and who approved the final result.
Create a permission policy for this AI workflow.
My recurring tasks:
[Describe the tasks]
Divide all activities into three categories:
1. AI may do this directly
2. AI may prepare a draft, but I must approve it
3. AI may never do this directly
For each activity:
- Give one specific example
- State when AI must stop and ask me
- Identify any irreversible action
- State which data or systems AI may access
- State which data or systems AI may never access
- State whether the action must be logged
- State what evidence must be retained for review
- State who is accountable for the final result
Pay particular attention to:
- Sending emails
- Deleting files
- Modifying production systems
- Purchasing anything
- Publishing publicly
- Contacting other people
- Making final decisions for me
- Accessing confidential information
- Using employee, customer, financial, or legal data
- Changing source data
- Approving transactions
- Creating external commitmentsPutting the Five Assets to Work
Once you have these five assets, one master prompt can bring them together into a reusable workflow.
I want to use you as an AI assistant that can complete complex work.
Do not execute the task yet.
Based on the materials I provide, create a reusable workflow.
Define:
1. The standard input
2. The standard output
3. The steps between input and output
4. Which steps AI can perform directly
5. Which steps require my approval
6. Which steps must remain human-led
7. The acceptance standard
8. The permission limits
9. The sources AI may use
10. The evidence that must be retained
11. A minimum working version I can test today
12. The risks I should resolve before increasing access or automation
My task:
[Describe the task]
My materials:
[List or attach the materials]Final Thought
Before assigning more tasks to AI agents, document what they need to do. Start with one defined task, one reliable input, one standard output, and one approval point. Run real examples through it. Compare the output with your accepted and rejected cases. Fix the gaps before adding more access, more steps, or more autonomy.
Someone who only collects prompts is asking the model to guess. Models, licenses, and platforms will keep changing. The value comes from turning what your team already knows into work that AI can repeat, people can review, and the business can rely on. It requires a clearly defined workflow, quality standards, the right sources and context, and clear points where AI must stop.
Give it only “help me with this,” and it can only guess. Give AI the scene, the materials, and the standards, and it can execute. This is when AI transformation moves from experimentation to practical business value.
* Author’s Note: All images in this article were created by the author using AI tools.
