An agent-ready task has a reproducible starting point, a bounded change surface, observable acceptance criteria, and enough repository guidance for the agent to validate its own work.
This guide focuses on the engineering decision behind agent ready coding tasks: what to standardize, what to constrain, and what evidence a reviewer should expect before accepting the result.
The core decision
Good candidates have a small number of files or one coherent subsystem, a failing test or crisp behavior statement, and no unresolved product choice. The issue explains what must remain unchanged as clearly as what should change.
A workflow that holds up in review
Use a readiness gate before dispatch: can another engineer reproduce the problem, name the likely verification commands, and review the expected diff in one sitting? If not, invest in discovery first. The discovery itself can be a separate agent task that returns a plan without code changes.
The failure mode to design around
Teams often delegate old tickets because they look self-contained. Old tickets are frequently missing current constraints and may point to deleted code. Refresh the reproduction and acceptance criteria before spending an agent run on them.
Implementation checklist
- Reproduce the current behavior.
- Name in-scope and out-of-scope areas.
- Specify pass/fail evidence.
- Keep one reviewable outcome per task.
Turn the checklist into operating controls
- Reproduce the current behavior: name the owner, the evidence that proves it happened, and the condition that should stop the run.
- Name in-scope and out-of-scope areas: name the owner, the evidence that proves it happened, and the condition that should stop the run.
- Specify pass/fail evidence: name the owner, the evidence that proves it happened, and the condition that should stop the run.
- Keep one reviewable outcome per task: name the owner, the evidence that proves it happened, and the condition that should stop the run.
The list becomes useful when every item produces visible evidence. Store that evidence with the task or pull request rather than in a private chat. A future reviewer should be able to tell which repository revision was used, which permission profile applied, what stopped or failed, and who accepted the remaining risk. For agent ready coding tasks, a short, complete record is more valuable than a long narrative that cannot be reproduced.
Move from one run to a repeatable practice
Introduce the practice with one task class and one repository. Compare the delegated path with the way the team completes the same work today, including waiting, review, and rework. Make the handoff visible in the issue tracker so the experiment does not create a second, private backlog. After several runs, write down which characteristics predicted success and which required live engineering judgment. That evidence should determine the next task class, not a general mandate to use an agent.
Before expanding the workflow, ask three review questions:
- What evidence shows that reproduce the current behavior was satisfied, and would that evidence survive a rerun from the recorded commit?
- What evidence shows that name in-scope and out-of-scope areas was satisfied, and would that evidence survive a rerun from the recorded commit?
- What evidence shows that specify pass/fail evidence was satisfied, and would that evidence survive a rerun from the recorded commit?
Write the answers in the same place as the code review. That creates a compact decision record and lets the team compare later runs without relying on memory.
A practical acceptance test
Run the workflow from a clean checkout at a recorded commit. Give the agent only the documented task packet and the intended permission profile. Then ask a reviewer who did not launch the run to reproduce the important checks, explain the changed behavior, and identify the rollback path. The task passes only when the artifact, evidence, and repository state agree. Keep the failed examples as regression cases; they are more useful than a polished demo because they reveal where instructions, environment, permissions, or tests need improvement.
Related reading
- Agentic Coding in the Cloud: The Complete Guide
- Anatomy of a Cloud Coding Agent Run
- Repositories, Sandboxes, and Pull Requests: The Cloud Agent Loop
Primary references
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