Engineering teams meet OKRs with justified suspicion, because the first draft is always the same: story points as Key Results. Velocity is the most gameable number in software. Estimates inflate, tickets split, the chart climbs, and nothing about the product or the system improved. The suspicion is earned; the conclusion that OKRs cannot work for engineering is not.
The fix is the same rule every function needs, applied strictly. A Key Result is a measurable outcome with a baseline and a target: fewer pages, faster recovery, quicker deploys, shorter onboarding. Output counts (points, tickets, PRs) describe motion. Outcomes describe whether the motion mattered.
Eight examples with realistic numbers for a mid-market B2B engineering org. Replace the figures with your own baselines before adopting any of them.
Reliability
Objective: Make on-call boring again.
- KR1: Cut pages per week from 31 to 8
- KR2: Reduce mean time to recovery from 94 minutes to 30
- KR3: Extend SLO coverage from 40% of production services to 90%
The page-count KR does double duty: it measures reliability and the humane cost of the rotation. Teams consistently underestimate how much attrition traces back to a loud pager. Expect the number to rise briefly when alert coverage improves before it falls; write that expectation down at kickoff so week-three panic does not kill the OKR.
Objective: Deploys so routine nobody watches them.
- KR1: Move deploy frequency from twice a week to daily
- KR2: Cut change failure rate from 18% to 5%
- KR3: Reduce median rollback time from 45 minutes to 5
Frequency alone is half an OKR; frequency with a falling failure rate is the actual claim, that speed and safety move together when the pipeline improves. If frequency rises and failures follow it up, the OKR has caught the team shipping faster than its safety net matured.
Delivery speed
Objective: Make the slowest user-facing path fast.
- KR1: Cut p95 latency on the checkout API from 2.1s to 800ms
- KR2: Reduce endpoints with p95 over 1 second from 23 to 4
- KR3: Catch 90% of performance regressions before production, up from 20%
Performance OKRs drift into micro-benchmarks that users never feel. Anchoring KR1 to one named, revenue-relevant path keeps the work where it counts, and the regression KR builds the mechanism that stops next year's slow creep.
Objective: Pay down the debt that taxes every single sprint.
- KR1: Cut CI build time from 28 minutes to 9
- KR2: Reduce flaky tests from 74 to 10
- KR3: Cut sprint capacity lost to unplanned rework from 25% to 10%
Tech-debt OKRs fail when they promise to "reduce debt" in the abstract. This one names two daily taxes every engineer pays and one outcome the roadmap feels. The rework KR needs honest sprint accounting, which is itself a habit worth the quarter.
Team and codebase health
Objective: A codebase a new engineer can ship to in week one.
- KR1: Cut time to first merged PR from 12 days to 3
- KR2: Bring services with a current runbook and named owner from 50% to 100%
- KR3: Reduce local environment setup from a full day to 30 minutes
Onboarding OKRs look soft until the hiring plan doubles the team. Time-to-first-PR is a systems metric wearing a people metric's clothes: it measures documentation, tooling, and review culture at once, which is why it is hard to game.
Objective: Security as a habit, not an audit-week panic.
- KR1: Reduce critical vulnerabilities open past 30 days from 9 to 0
- KR2: Cut dependencies more than 12 months out of date from 140 to 20
- KR3: Bring services with a current threat model from 15% to 80%
Security OKRs convert an annual compliance scramble into quarterly hygiene. The dependency KR is the sleeper: stale dependencies are both a security surface and the hidden reason upgrades take weeks. Expect this OKR to surface unowned services; that discovery is a result, not a distraction.
Platform and data
Objective: A data platform other teams trust without checking.
- KR1: Cut pipeline failures from 12 per week to 2
- KR2: Bring dashboards with a named owner and freshness SLA from 30% to 95%
- KR3: Reduce data-incident tickets from 40 to 8 per quarter
Trust in data is binary in practice: one wrong executive dashboard and every number gets re-verified by hand for a quarter. The ownership KR attacks the root, which is that most broken dashboards are orphans nobody was responsible for noticing.
Objective: Retire the legacy service without a big-bang cutover.
- KR1: Move production traffic on the new service from 0% to 80%
- KR2: Retire 45 of the 60 legacy endpoints
- KR3: Keep migration-attributable incidents at 2 or fewer for the quarter
Migration OKRs keep multi-quarter work honest by forcing a measurable slice into each quarter. The incident cap belongs in the OKR because it prices the constraint everyone claims to honor and nobody writes down: momentum does not justify breaking production.
Adapting these to your team
Baselines first, targets second, and never adopt a number because a blog post used it. Two or three Objectives per team per quarter, with the roadmap living underneath them as the supporting work. The OKR template uses this baseline-target structure, the OKR grader flags output-shaped Key Results like story points, and how to pick the right goal framework is the upstream question if OKRs are not settled yet.
Companion example sets: marketing OKRs, product OKRs, and sales OKRs.
What the sprint board will not tell you
Engineering OKRs fail through reallocation. The flaky-test work is real for two sprints, then an escalation eats the team, and the KR sits at 60 flaky tests while the sprint board shows everyone fully loaded on other work. The board shows activity; it does not show that this Key Result no longer has anyone behind it. That gap between the target and the pace of the work still pointed at it is execution risk. Teams that connect their OKRs to the actual engineering work in Vindaris see the moment a Key Result loses its work, in the week it happens rather than in the retro.