- Codex pilot
- Measure accepted diffs, CI pass rate, review time, permission friction, parallel-task throughput, and credit or plan usage across the surfaces your team will actually use.
- Devin pilot
- Measure task acceptance rate, PR quality, CI pass rate, human cleanup time, shared usage consumption, and whether tickets are scoped tightly enough for delegation.
- Fair side-by-side trial
- When both tools are plausible, compare them on the same task class and risk tier. Hold the repository, tests, acceptance criteria, and reviewer constant, then compare accepted output, elapsed time, review and rework effort, and usage cost so different task mixes do not decide the result for you.
- Pre-registered evaluation rubric
- Before the first side-by-side run, write down the acceptance thresholds and scoring weights for output quality, elapsed time, reviewer and rework effort, recovery failures, and accepted-work cost. Use the same rubric for Codex, Devin, and the human baseline. If the team changes a criterion after seeing results, record the change and rerun the affected sample instead of letting moved goalposts pick the winner.
- Human baseline gate
- Keep a small no-agent baseline for the same task class before declaring either agent the winner. Compare accepted output, elapsed time, reviewer effort, rework, and total accepted-work cost against how the team completes similar work today. If neither Codex nor Devin beats that baseline without weakening quality or controls, keep the current workflow instead of forcing a binary tool choice.
- Delegation-readiness gate
- Track every meaningful mid-task clarification, redirect, or scope correction during the same pilot task class. If delegated work repeatedly needs human steering before it reaches review, keep that task class in a hybrid engineer-controlled workflow or tighten its acceptance criteria before granting more autonomy. Count that intervention time in accepted-work cost instead of treating it as free supervision.
- Task-preparation cost gate
- Measure the human work before either agent starts: gather repository context, clarify the ticket or spec, write acceptance criteria and test commands, arrange access, and brief the reviewer. Add that preparation time to accepted-work cost for the same task class. If one tool only wins after unusually detailed packaging, count that setup burden as part of the operating-model decision instead of free overhead.
- Backlog-representativeness gate
- Before comparing pilot results, define the mix of task classes, complexity, and risk that actually makes up the backlog you expect the agent to handle. Sample both tools against that mix, and record tasks you exclude, abandon, or hand back to humans. If one tool only wins on a cherry-picked slice of unusually clean tickets, do not generalize that result to broader seats or autonomy.
- Repeatability gate
- Do not let one unusually good run decide the pilot. Repeat each representative task class enough times to expose variance in accepted output, elapsed time, reviewer effort, rework, and failures. If the apparent winner changes from run to run or depends on a single outlier, collect a larger sample or narrow the approved task class before expanding seats or autonomy.
- Repository-generalization gate
- Do not turn a pilot win in one unusually agent-friendly repository into an organization-wide standard. Before expanding seats or autonomy across repositories, repeat a bounded sample across the repository shapes the team actually maintains, such as different languages, test maturity, dependency depth, and build or deployment complexity. Keep the task class and risk tier comparable, and narrow the approved rollout when the apparent winner does not generalize.
- Failure-recovery rehearsal
- Include at least one low-risk pilot task where the agent gets blocked, fails validation, or starts from incomplete context. Require the team to stop or redirect the run, recover a clean branch or workspace, and hand the task back to a human or the other agent. Count recovery time, discarded work, and reviewer interruption in accepted-work cost; a tool that wins only on happy-path tickets has not earned broader autonomy.
- Risk-tier delegation gate
- Define which task classes each agent may receive before the pilot starts. Keep low-risk work on a standard review path, and require explicit human approval or keep work out of scope when it touches production data, authentication, payments, security controls, destructive migrations, or other high-blast-radius changes. Compare Codex and Devin only inside the same risk tier so broader authority does not masquerade as better agent performance.
- Parallel-work isolation
- For either tool, isolate concurrent tasks by branch, worktree, or another non-overlapping workspace, assign one accountable reviewer per task, and define merge order before scaling. Count collision cleanup and duplicate work as pilot costs rather than free throughput.
- Post-pilot scale gate
- Do not turn a successful task sample directly into broad repository access or more seats. Before scaling either tool, name the rollout owner, preserve branch protection and human review, set a usage budget and stop condition, and explicitly approve any broader repository, integration, secret, or network scope.
- Reviewer-capacity gate
- Do not scale parallel agent work faster than reviewers can absorb it. Set a maximum concurrent agent-work queue for the pilot, track time to first review and cleanup or rework per accepted PR, and reduce concurrency or seats when review age or rework rises even if agent task throughput looks strong.
- Accepted-work cost gate
- Do not scale from sticker price, credits consumed, or agent throughput alone. For the same task class, compare total pilot cost per accepted change: tool or usage spend plus reviewer time, rework, collision cleanup, failed-task cleanup, recovery effort, and mid-task human intervention. Scale the option that reduces accepted-work cost without letting review age, quality, or risk controls deteriorate.
- Post-merge durability gate
- Do not treat merge as the end of the pilot measurement window. Recheck a small sample of accepted agent changes after they have lived in the codebase long enough to expose regressions, reverts, follow-up fixes, or unexpected maintenance work. Count that downstream work against the original task before expanding seats or concurrency; an agent that looks efficient at merge time but creates more maintenance later has not proved lower accepted-work cost.