How to Delegate to an AI Agent: The 5-Part Task That Comes Back Done

Continue Press · July 2026 · Pillar: managing AI agents

Here's an uncomfortable truth about autonomous agents: when the output disappoints, the task was usually the problem. Agents fill every unspecified dimension with a plausible guess — and plausible guesses, multiplied across a whole task, land you eleven degrees off target with total confidence.

The fix isn't prompting tricks. It's the same skill managers need with human employees, made slightly more explicit: a delegation grammar. Five parts, learnable in ten minutes, and the difference between work that comes back done and work that comes back as a question (or worse — done, wrong, and polished).

The anatomy: five parts, every task

Compare two versions of the same wish:

"Work on marketing."

"Write 3 candidate launch descriptions for the product page (do not publish — guardrail). Each: different angle, under 300 words, honest about being a solo project. Success: I can read all three in 5 minutes and pick one. Output → projects/launch/descriptions.md."

The second version has all five parts:

  1. A verb with an end. "Write 3 descriptions," not "work on." Tasks that can't finish, don't.
  2. Scope pinned down. How many, how long, what tone. Every unpinned dimension is a coin-flip you're delegating to chance.
  3. The success test. How will we know it's done and good? This single element improves output quality more than anything else — partly because it forces you to know what you want, which is, uncomfortably often, the real bottleneck.
  4. Boundaries restated where they bite. The "do not publish" isn't redundant with your standing contract — it's a courtesy flag at the exact spot where an eager agent might trip.
  5. A landing place. Output goes somewhere named, so tomorrow's session (and you) can find it.

Five parts sounds bureaucratic. In practice it's three sentences, and the habit forms in a week.

The altitude rule: delegate outcomes, not keystrokes

Precision has a failure mode of its own — specifying too low:

"Open the browser, search for X, read the top 20 results, make a table with columns A, B, C…"

If you're dictating steps, you've already done the thinking and you're renting typing. The right altitude for an autonomous agent is the outcome, its constraints, and the success test — the how is what you hired it for:

"Find out what frustrates buyers of [product category] (source: public reviews/threads). Success: a one-page summary of the top 5 complaints, each with 2+ direct quotes. → research/complaints.md"

Outcome-level tasks regularly come back better than you'd have specified, because the agent tries angles you wouldn't have. When they come back worse, the success test catches it — and your correction becomes a recorded principle that improves every future task. That's the loop improving itself.

Priorities are a budget, not labels

If you run a backlog (you should — it's the file that replaces "what should I do?" forever), two rules keep it honest:

And give new ideas a parking lot separate from commitments. Ideas get promoted to tasks only during a review, in cold blood, by asking: does this beat the current P1 on evidence, not on novelty? Novelty always feels like it wins. That's what novelty is.

The one-sentence template

For the notes file:

"New task, P1: [verb-with-an-end + scope]. Success: [the test]. Boundaries: [where the rails bite]. Output → [landing place]."

Do this now

Take the vaguest task you've ever given an AI (there is one — there's always one) and rewrite it with the five parts. Then run it. The before/after on output quality is the fastest way to convince yourself that delegation, not model choice, was the bottleneck all along.

Delegation is one chapter of a bigger system

Your AI Employee: The Playbook + Template Pack covers the full operation: persistent memory, the job contract, guardrails, metrics that don't lie, and three pre-built agent configurations — with 17 ready-to-paste files.