That model assumed a human engineer would pick up a ticket, interpret the intent, fill in the gaps, ask questions, and figure out how to implement it.

AI coding agents change that assumption.

An agent doesn’t bring the same context or judgment as an experienced engineer. It needs a much clearer definition of the problem, the expected outcome, the boundaries of the work, and how success will be verified.

The backlog’s purpose hasn’t changed. Its consumer has.

From work tracking to executable work

A useful work item for an AI agent needs more than a title and a few acceptance criteria. It should answer four questions:

• Why: What customer problem or business outcome are we addressing?
• What: What should change, and what is explicitly out of scope?
• How do we know: What can be tested or verified when the work is complete?
• How big: Is the work appropriately scoped for an agent to execute and a human to review?

That last question matters more than it might seem. Generating code is becoming cheaper and faster. Reviewing, validating, and trusting the result still takes human attention.

A large task may be easy for an agent to attempt but expensive for a person to evaluate. Smaller, well-defined work items create a more manageable flow from execution to review.

Human judgment becomes the constraint

AI doesn’t eliminate the need for product judgment. It changes where that judgment is applied.

Product managers still need to understand customer problems, decide what matters, and set priorities. They also need to ensure proposed work is worth doing before an agent starts, and review the result before it ships.

The goal isn’t to let agents run the product roadmap. It’s to keep them working on the highest-value tasks while preserving human control over decisions and quality.

This introduces a new operating constraint: how much agent work can your team responsibly review?

The most effective teams will balance the amount of work agents execute with the capacity of people to evaluate and approve it.

A different way to manage the backlog

Imagine a backlog that continuously absorbs customer signals from calls, support tickets, and team conversations. Those signals become proposed work, sized and prioritized for value. Agents pick up approved tasks, execute them, and return results for human review.

The backlog becomes more than a record of what the team intends to do. It becomes the operating queue for continuous product execution.

That’s the idea behind ProductMaxxing: keep your team’s AI agents working on the most valuable backlog items they can complete, paced by the team’s capacity to review their work.

The objective isn’t to maximize the amount of code AI produces. It’s to maximize the amount of valuable product work a team can confidently deliver.

The backlog was built for humans. It’s time to make it work for agents, too.