I have become more skeptical of backlog items that say an agent can just figure it out. The newer cloud coding agents are genuinely useful, but they still convert product ambiguity into code churn. If a ticket would be vague for a junior engineer, it is usually worse for an agent, because the agent can produce a confident branch before anyone notices the missing decision.
Why issue quality is now product delivery work
GitHub now describes Copilot cloud agent as a background worker that can research a repository, create an implementation plan, make changes on a branch, run tests and linters, then optionally open a pull request. OpenAI describes Codex similarly: separate cloud tasks, repository context, command execution, test output, and human review before integration.
That changes the product manager's job in a subtle way. The ticket is no longer just a reminder to a human teammate. It becomes the handoff contract for a worker that can spend compute, open branches, create reviewer load, and collide with other work.
I wrote recently about rolling out CLI coding agents as an engineering practice, especially after the Microsoft rollout data showed that adoption and output are not evenly distributed across teams. The product-delivery layer is the next constraint. Teams do not get leverage from agents by assigning more tickets. They get leverage by making more tickets safe to delegate.
A good agent-ready issue is not a huge spec. The recent paper "What Makes a GitHub Issue Ready for Copilot?" is useful precisely because its results cut against that instinct. The authors found better merge outcomes associated with shorter, well-scoped issues that include clear guidance, relevant artifacts, and implementation hints. They also found that issues leaning on external references, environment setup, dependencies, or external APIs had lower merge rates.
My takeaway: make the ticket self-contained, not exhaustive.
The checklist I would use before assignment
1. State the user-visible change in one sentence
The first sentence should be boring and concrete. Not "improve onboarding". Say "When a new workspace has no projects, show an empty state that links to project creation." If the first sentence needs a meeting to decode it, the agent should not start.
This is also where I separate discovery from delivery. If the product question is still open, write a research task for a human or ask an agent to investigate without code. Do not disguise uncertainty as an implementation ticket.
2. Bound the work to one path through the product
Agent tickets fail when they contain three adjacent products wearing one title. Pick one workflow, one user role, one entry point, and one expected result.
For example:
| Weak ticket | Agent-ready version |
|---|---|
| Improve billing settings | Add the missing invoice download empty state to billing settings |
| Make search better | Preserve the current query when switching from results to filters |
| Add onboarding analytics | Emit one event when the first project is created from the empty state |
The narrower version is not less ambitious. It is more mergeable.
3. Name the relevant files or components
A human can infer where to look from history, team memory, and Slack context. An agent usually needs navigational hints. Point to the route, component, model, migration, test file, or existing pattern that matters.
This does not mean prescribing the exact patch. I prefer hints like "follow the empty-state pattern in ProjectListEmptyState" or "the validation lives near the existing workspace name schema". That gives direction without freezing the implementation.
4. Define acceptance criteria as checks, not vibes
Acceptance criteria should be reviewable by a person who did not write the ticket. I like three layers:
- Product behavior: what the user sees or can do.
- Regression boundary: what must not change.
- Verification: the test, lint, build, screenshot, or manual path that proves it.
GitHub's own guidance emphasizes clear descriptions, acceptance criteria, relevant files, scoped tasks, and avoiding ambiguous or sensitive work. The paper on issue readiness adds empirical weight to that guidance. The work should be small enough that a reviewer can tell whether the branch did exactly the requested thing.
5. Declare what is off limits
Agents are eager generalists. That is useful for exploration and dangerous for delivery. Every ticket should include a short non-goal when the surrounding area is tempting.
Examples:
- Do not redesign the billing page layout.
- Do not change the pricing calculation.
- Do not add a new analytics provider.
- Do not touch auth policy or role checks.
This is especially important for product areas with money, permissions, privacy, or compliance. Those are not banned from agent work, but they need tighter human ownership and usually a smaller first slice.
6. Coordinate parallel agent work like parallel human work
The freshest warning sign is not model quality. It is coordination. A July 2026 study of AI agent pull requests found that agent-authored PRs often overlap in the same repositories, and replayed merges showed higher textual conflict rates for cross-agent pairs than intra-agent pairs.
That maps directly to product planning. If I assign five agents to adjacent settings-page tickets, I should expect conflict unless I split by route, component, or module ownership. The backlog needs a concurrency rule, not just a priority order.
A simple rule works: one agent per product surface unless the touched files are named up front and clearly disjoint. If two tasks touch the same component, sequence them or ask the first agent to produce a plan only.
The ticket template
This is the shape I would use for routine product work:
## User-visible change
One concrete sentence.
## Scope
- Entry point:
- User role:
- Files or components likely involved:
## Acceptance criteria
- Behavior:
- Regression boundary:
- Verification:
## Non-goals
- Do not:
## Review notes
- Risk:
- Suggested reviewer:
The important part is not the template. It is the decision to treat issue writing as delivery design. The PHBench and YC Bench post I published earlier was about choosing better startup signals. This is the same muscle at the team level: pick signals that predict whether work will actually land.
What I would assign first
I would start with documentation updates, test coverage, narrow UI states, small refactors, and low-risk product polish. GitHub lists documentation, test coverage, incremental features, technical debt, and merge-conflict resolution as agent-suitable tasks. OpenAI's Codex examples also point toward well-scoped refactors, tests, integration failures, and background work.
I would be slower with ambiguous product flows, permission changes, payment behavior, data migrations, and anything where correctness depends on unstated company policy. For those, I would ask an agent to inspect the codebase and propose options first, then let a human choose the product decision.
The near-term advantage will not come from dumping the backlog into agents. It will come from teams that learn to write smaller, sharper issues and reserve human attention for the decisions that still deserve it.