What’s the right granularity of tasks for AI agents to be effective but controllable?

The myth of the perfect to-do list dies hard.

We’ve spent decades trying to break work into neat little boxes — tasks, tickets, OKRs — in hopes that the right shape of structure will magically drive performance. Now that AI agents have entered the chat, the same question is back with a twist: how small is too small when handing off work to machines?

Because here’s the thing: if your task is too vague, the AI flails. But if it’s too specific, you end up micromanaging your robot.

There’s a sweet spot. And finding it isn’t just a technical question. It’s a leadership one.

Most companies are still playing with Legos

Let’s start with an example.

Say you’re building a competitive intelligence report. You could theoretically tell an AI agent, “Hey, analyze the last two years of earnings calls from our top three competitors and tell me what their go-to-market priorities are.”

Depending on what stack you’ve got wired up behind the curtain, the agent might hallucinate a strategy out of thin air — or choke entirely.

So you slice it up. One agent fetches transcripts. Another runs entity extraction. A third summarizes themes. You stitch them together with scaffolding and hope the final product isn’t a Frankenstein.

That’s progress, sure. But it’s fragile.

The moment one input changes — a new competitor, a different file format — the whole thing wobbles. That’s because we’re still thinking like engineers: break the problem into smaller problems until the computer can handle it.

But AI agents aren’t Legos. They’re interns who talk a big game.

Think workflows, not tasks

A more useful lens: don’t ask, “What’s the right size for a task?” Ask, “What’s the right level of autonomy for this context?”

Because AI agents don’t just execute. They reason. Or at least, they try to.

The real magic of these systems isn’t that they can follow instructions — it’s that they can make decisions about how to follow them.

That shifts the question from granularity to delegation.

Let’s go back to that competitive report. Give a well-structured agent system a clear goal — “Summarize strategic priorities from the last eight quarters of earnings calls” — and it might decide for itself: fetch the transcripts, filter for key segments, analyze language patterns, compare results across time.

If (and that’s a big if) the underlying agents are well-coordinated, you get a report that’s not just scraped and half-summarized, but genuinely insightful.

The trick is deciding how much decision-making you’re willing to offload — and when to yank the wheel.

Control isn’t about task size — it’s about checkpoints

Here’s the paradox: more freedom makes agents smarter, but also harder to trust.

When you slice work too finely, the agent can’t reason usefully. “Summarize this document” doesn’t let it ask, “Why? For whom? In what tone?”

But when you hand it open-ended commands, you risk a wordy mess or worse — confidently delivered nonsense.

The way out isn’t endless prompt engineering. It’s structuring work around outcomes with built-in checkpoints. Think of it like managing a junior hire:

• You don’t tell them every keystroke
• But you do ask for a rough draft before they spend 10 hours going off the rails
• You define done — clearly — and surface their assumptions along the way

For AI agents, that looks like a blend of autonomy and auditability.

Give the agent room to operate, but embed self-checks: validate inputs, surface intermediate steps, summarize progress in ways humans can inspect quickly.

This isn’t micromanagement. It’s mentoring — at machine scale.

Abstractions are power tools (and also hazards)

Let’s zoom out.

The ultimate goal of AI agents isn’t task automation. It’s abstraction.

You should be able to say, “Monitor our top five competitors and alert me if any of them announce a shift in pricing strategy,” and have the system figure out what that entails — without you hardcoding every step.

That’s possible. But only if you’re willing to build agents that operate at the level of strategic intent.

And that means getting comfortable with:

1. Defining outcomes in human terms that map to ambiguous real-world goals
2. Letting agents decide which “tasks” to invent and sequence internally
3. Giving yourself visibility into how they got there

The danger? These abstractions collapse under scrutiny if you don’t harden the foundations.

It’s like trying to build a skyscraper out of particleboard. Looks great for a floor or two. Then it buckles.

You need durable subroutines. Safe fallbacks. Clear handoffs between agents. All of which require real engineering — not just clever prompting.

But once you’ve got them, the payoff is huge: software that works more like a team than a toolbox.

What this really means for leaders

If you’re waiting for a universal answer to “how big should tasks be for AI agents,” stop. That’s the wrong game.

Instead, ask better questions:

• What’s the minimum context an agent needs to make good decisions?
• Where do I need control — and where does control actually slow us down?
• Can we design checkpoints early enough to catch bad paths, without choking autonomy?

The future isn’t a hierarchy of tasks. It’s a mesh of agents — and humans — collaborating toward goals, at different levels of abstraction.

Design for that, and you won’t need a perfect to-do list.

Just a shared understanding of where you’re headed — and how to know when you’ve gone off course.