Most AI-assisted tasks are still written as if there is only one reader. Sometimes the task is written for the developer. Sometimes it is written like a prompt for the AI agent. That approach starts breaking down when a teammate and an AI coding agent are expected to complete the work together. There are actually two readers. They need different information.
THE FIRST READER IS THE AI AGENT
The coding agent needs the detailed working brief.
For a software task, that can include the intended outcome, repository context, relevant files, technical constraints, existing patterns, tests, known edge cases, and things that must not change. This part of the task can be detailed. That is useful. The agent does not need a short motivational summary. It needs enough information to avoid guessing. If there is an existing helper that must be reused, say so. If changing a particular API is outside the scope of the task, say so. If the result must work on mobile as well as desktop, say so. If a specific test suite needs to pass, include it.
Missing context does not make an agent more flexible. It usually makes the result less predictable.
THE SECOND READER IS THE HUMAN
The person supervising the task has a different job. They probably do not need to reread every implementation detail. They need operational instructions. Which environment should they open? Which agent should they use? What should they inspect? What result are they responsible for judging? What should cause them to stop? When should they ask the task author for help? These are human instructions. They are different from the technical prompt sent to the coding agent.
ONE GIANT PROMPT IS USUALLY WORSE
A common approach is to put everything into one large description. That creates a compromise document. The human starts skimming because the task contains too much technical detail. Important supervision instructions become buried. Then task authors start shortening the description because nobody wants to read it. Now the AI agent loses useful context. Both readers receive a worse version of the task. Separating the instructions is cleaner. The agent gets what it needs for execution. The human gets what they need for supervision.
A SIMPLE EXAMPLE
Imagine the task is:
"Add CSV export to the invoice table."
That sentence sounds clear until someone actually starts implementing it. The coding agent may need to know that the export must respect active filters. It may need to use the existing invoice query. It may need to preserve current date formatting. It may need to escape commas and quotation marks correctly. It may need to include invoice IDs. It may need to add tests. It may also need an explicit instruction not to modify invoice calculations. Those are agent instructions. The human runner needs something different. They may need to open the test account. Generate several CSV files. Open them in a spreadsheet. Check totals. Check accented names. Check filtering. Verify that the expected rows are present. Stop if tax calculations appear to have changed. The two instruction sets point toward the same result. They simply describe different responsibilities.
THIS MAKES AI TASKS EASIER TO HAND OFF
Once tasks are structured this way, the original author does not need to supervise every agent session personally. They can put their technical knowledge into the task. Another teammate can run it. That teammate does not need to reconstruct the author's reasoning from Slack messages, old tickets, and repository history. The AI gets the implementation context. The human gets the operating instructions. A reviewer can inspect the evidence afterward. That is much closer to a proper work handoff. Wagglet uses this kind of two-audience approach for AI task handoffs. https://wagglet.com
The idea is useful because it treats the human runner and the AI agent as separate participants instead of assuming they need the same prompt.
HUMAN INSTRUCTIONS SHOULD INCLUDE STOP CONDITIONS
One of the most useful things a task can contain is a clear stopping rule.
For example:
Stop if the migration requires deleting existing records.
Do not change authentication behavior.
Escalate if the required API is undocumented.
Do not use production customer data.
Ask the task author if the implementation needs a new permission.
These rules matter because AI agents are generally optimized to keep moving toward completion.
That can be useful.
It can also create problems when the task reaches an unexpected boundary.
The human supervising the work needs to know when continuing is no longer the correct action.
THE AGENT NEEDS BOUNDARIES TOO
Stop conditions should not exist only in the human instructions.
The agent should also understand the task boundaries.
If database schema changes are out of scope, say so.
If introducing a new dependency requires approval, say so.
If existing public API behavior must remain unchanged, make that explicit.
A good task does not only explain what should happen.
It also explains what should not happen.
DELIVERY SHOULD PRODUCE EVIDENCE
A strong AI task should define what the final delivery needs to contain.
"Tell me when it is done" is too vague.
A better delivery might include the files changed, tests run, test results, screenshots, known limitations, and anything the runner could not verify.
This gives the reviewer something concrete to inspect.
It also makes weak task completion easier to spot.
If the task requires screenshots and no screenshots were provided, the delivery is incomplete.
If the task requires a specific test suite and the runner cannot confirm it was executed, that is visible immediately.
THE HUMAN SHOULD HAVE A REAL JOB
There is a bad version of human oversight where the person simply launches the agent and approves whatever comes back.
That is not meaningful supervision.
The human should have a responsibility the AI cannot simply declare complete on its own.
They might inspect a visual result.
Confirm behavior in a real browser.
Check a staging environment.
Judge whether a user flow makes sense.
Review a security-sensitive change.
Compare the result against a design.
The human role should be explicit.
Otherwise "human in the loop" becomes a label rather than an actual control.
BETTER AI WORK STARTS BEFORE THE AGENT RUNS
There is a lot of attention on model choice.
Teams compare coding agents, context windows, benchmarks, and subscription plans.
Those things matter.
But many failures start before the model is ever involved.
The task is vague.
Nobody wrote down the constraints.
The human role is undefined.
Success is subjective.
The reviewer receives a confident completion message instead of useful evidence.
Writing the task for two readers fixes a surprising amount of this.
Give the AI the technical brief.
Give the human the operating instructions.
Give both clear boundaries.
Then define what evidence must exist before the work can be considered complete.
That produces a much stronger unit of AI-assisted work than a prompt pasted into a coding chat.
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