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Engineering OKR Examples: Beyond Velocity Theater
- Daniel Madhan
- Aug 24
- 10 min read
Engineering OKR: At first glance, an engineering team might seem productive. However, the software created by the team may prove to be slow, unreliable, or more complicated to maintain. That is the reason why engineering managers need OKRs that are business-driven, customer-oriented, and not activity-driven. The best OKRs for engineering teams connect technical tasks with reliability, speed, quality, and experience.
So, let's talk about how to build engineering OKRs that don't just look good on a slide but actually shift the way your team works, thinks, and builds.
Output Metrics vs Outcome Metrics
Story points and deploys are outputs. They inform you of what the team did. They don't tell you whether the work improved the product, the system, or the business.
Outcomes are the feelings the org actually experiences. As the API speeds up, the customer feels it. If deployments do not break things, the product manager feels it. When new hires ship code in their first week, the whole team feels it.
The actual test is: If we hit this KR, will anyone outside this room know? If the answer is no, you are monitoring output.
TABLE 01 · THE OUTSIDER TEST
Output tells you what happened. Outcome tells you who felt it.
Output metric | Outcome metric | |
Speed | Story points burned | p99 latency 840ms → under 150ms |
Reliability | Deploys per sprint | MTTD 18 min → under 3 min |
Quality | Unit test coverage 60% → 75% | Incident response 6 hrs/wk → 1.5 |
Platform | Services migrated | Build time 25 min → under 5 |
Anyone outside the room notices | No | Yes |
Google's OKR framework is rated 0.7 because goals should be stretch goals rather than guaranteed wins. If you are achieving 1.0 on all KRs each quarter, you need to set more challenging targets. You're sandbagging, and your team isn't getting better.
TABLE 02 · STRETCH CALIBRATION
What your score distribution is telling you
Consistent 1.0 | Landing near 0.7 | |
What it signals | Sandbagging | Properly ambitious targets |
Team growth | Flat | Compounding |
Target-setting next quarter | Raise the bar | Hold the calibration |
Risk appetite | Zero | Deliberate |
Reliability OKRs
Reliability OKRs are used to determine if your system remains up and running and how quickly it recovers when it fails. The secret of this is to shift from firefighting to boring operations.
Objective: To make the system tedious to use
Key Results:
Reduce p99 API latency from 840ms to under 150ms for the three highest-traffic endpoints.
Reduce the Mean Time to Detect for P1 incidents from 18 minutes to less than 3 minutes.
Improve the core platform uptime from 99.2% to 99.95% (current: 99.2%)
Why this works: These KRs force genuine progress. You can't hit lower latency without making real changes to your code and architecture. You can't reduce MTTD without sharpening your monitoring and alerting. And the uptime target holds everything together it makes sure the first two goals actually translate into measurable results.
A common mistake: Setting an uptime target without an error budget. A 99.9% uptime goal gives you roughly 43 minutes of allowable downtime per month. Use that time wisely if you're not spending your budget, stay calm and keep going. But if you're burning through it fast, reliability takes priority and everything else pauses.
TABLE 03 · RELIABILITY KR ANATOMY
Each target forces a different kind of work
Key result | What it forces | |
p99 API latency | 840ms → under 150ms | Real code and architecture change |
MTTD for P1 incidents | 18 min → under 3 min | Sharper monitoring and alerting |
Core platform uptime | 99.2% → 99.95% | Ties the first two to a measurable result |
Missing piece | Uptime target with no error budget | No rule for when reliability wins |
TABLE 04 · ERROR BUDGET
What each uptime target actually buys you per month
Uptime target | What it forces | |
99.0% | Roughly 7 hours 18 min | Real code and architecture change |
99.5% | Roughly 3 hours 39 min | Sharper monitoring and alerting |
99.9% | Roughly 43 minutes | Ties the first two to a measurable result |
99.95% | Roughly 22 minutes | No rule for when reliability wins |
99.99% | Roughly 4 minutes | No rule for when reliability wins |
Delivery OKRs
Delivery OKRs are about the speed of delivery and the frequency of deployments that fail. That's where DORA metrics come into play they're the industry standard because they predict organizational performance.
Objective: Make shipping feature feel fast and safe
Key Results:
Make releases more often (2x/week to daily).
Reduce rollback rate from 11% of deployments to less than 2%.
Decreased the time from "PR opened" to "merged and deployed" from 4.5 days to less than 6 hours for 80% of PRs.
Why this works: Deployment frequency and lead time are measures of throughput. The rollback rate is a measure of quality. Adding more frequency and not reducing the rollbacks is simply making things break faster. The PR to production metric is a full measure of the developer experience, from code review bottlenecks to CI pipeline slowdowns and deployment friction.
TABLE 05 · DORA FRAMING
Throughput and quality have to move together
Measures throughput | Measures quality | |
Deployment frequency | Yes | No |
Lead time for change | Yes | No |
Rollback / change failure rate | No | Yes |
If you improve only throughput | — | You break things faster |
I once helped a team to reduce this from 4.5 days to under 6 hours by implementing automated PR reviewers, parallelizing CI pipelines and automating deployments. It wasn't a heroic change, it was a systematic change.
TABLE 06 · PR TO PRODUCTION
One metric that exposes the whole developer experience
Before | Measures quality | |
Time from PR opened to deployed | 4.5 days | Under 6 hours for 80% of PRs |
Code review bottleneck | Manual assignment | Automated PR reviewers |
CI pipeline | Sequential | Parallelised |
Deployment | Manual steps | Automated |
Nature of the change | — | Systematic, not heroic |
Quality OKRs
Quality OKRs measure defects, on-call burden, and the effectiveness of your testing. The trap here is measuring test coverage as a vanity metric. 95% test coverage does not matter if the tests are not testing the right things.
Objective: Reduce the invisible tax of poor quality on engineering time
Key Results:
Reduce time spent per engineer on incident response from 6 hours/week to under 1.5 hours.
Automate 90% of routine deployment checks that currently require manual verification.
Reduce "where's the runbook for X?" Slack questions to the platform team by 70%.
Why this works: The problem is that measuring test coverage is wrong; measure the impact of poor quality the time your engineers spend on firefighting and manual verification. If load on call decreases, you know there's quality improvement. If the questions in your runbook go away, then you are successful in your documentation and automation efforts!
TABLE 07 · QUALITY: VANITY VS IMPACT
Measure the tax, not the activity
Vanity measure | Impact measure | |
Testing | 95% test coverage | Bugs found before production |
On-call | Number of alerts configured | 6 hrs/wk → under 1.5 per engineer |
Deployment checks | Checklist exists | 90% automated |
Documentation | Pages written | Runbook questions down 70% |
Who benefits | The slide | The engineer's week |
A quality objective that fails: "Increase unit test coverage from 60% to 75%." This is an activity! It doesn't say anything about bugs being discovered or about incidents being prevented. Test coverage is not a product, but a planning tool. Your users don't give a hut about your test coverage percentage.
TABLE 08 · THE FAILING OBJECTIVE, DIAGNOSED
Why “60% → 75% coverage” does not hold
The problem | Impact measure | |
Type of measure | Activity | Outcome |
Says anything about bugs found | No | Yes |
Says anything about incidents prevented | No | Yes |
What coverage actually is | A planning tool | Not a product |
User impact | None | Direct |
Platform and Developer Experience OKRs
Platform team OKRs are notoriously difficult because you are building for internal customers. Your "customer" is other engineers. The metric that matters is how much friction you remove from their day.
Objective: Unblock engineers from shipping
Key Results:
Reduce build time from 25 minutes to under 5 minutes.
Reduce developer onboarding time from 4 weeks to under 5 days.
Increase self-serve adoption of platform tooling from 40% to 85% of all infrastructure requests.
Why this works: Build time is the most visible friction point. Every engineer feels it. Onboarding time multiplies with every new hire if you are scaling from 40 to 65 developers, cutting onboarding time saves hundreds of hours. Self-serve adoption reduces the platform team's workload and gives engineers autonomy.
TABLE 09 · PLATFORM AND DEVELOPER EXPERIENCE
Friction removed, not migrations counted
Key result | Why it compounds | |
Build time | 25 min → under 5 min | Most visible friction; every engineer feels it |
Onboarding time | 4 weeks → under 5 days | Multiplies with every new hire |
Self-serve adoption | 40% → 85% of requests | Cuts platform load, gives engineers autonomy |
The anti-pattern | Number of services migrated | Tracks activity, not leverage |
A platform team that focuses on these metrics becomes a force multiplier. A platform team that measures "number of services migrated" is just tracking activity.
The Key Result That Fails First
The most dangerous KR is often the one nobody watches until an incident happens.
You may find that deployment frequency is increasing, lead time is decreasing, and the engineer's confidence in the deployment process is decreasing. Individuals begin to add manual checks, exclude some services, postpone high-risk releases, or hold back fixes from production.
Those behaviors are early warning signs.
Your trigger should sit close to the work. Some of the signals that could be useful are: rising blocked time, repeated missed updates, growing dependency age, loss of confidence, and the discrepancy between reported progress and actual movement.
TABLE 10 · EARLY WARNING SIGNALS
What to watch before the incident, not after
Signal | What it usually means | |
Rising blocked time | Work is queued behind something | Dependency forming |
Repeated missed updates | Silence, not slow progress | Owner has disengaged |
Growing dependency age | A blocker nobody has touched | Cascade risk |
Falling confidence | Team knows before the number does | Leading indicator |
Reported progress ≠ actual movement | Status is being managed | Score is inflated |
This is where an execution layer like ShiftFocus comes into play. It's about catching execution drift and directing interventions before a goal is a quarter-end miss.
TABLE 11 · WHERE AN EXECUTION LAYER FITS
Catching drift before it becomes a quarter-end miss
Without an execution layer | With one | |
Drift detected | At the incident or the review | 2–3 weeks early |
Trigger location | Far from the work | Close to the work |
Signal type | Self-reported status | Blocked time, dependency age, confidence gap |
Response | Discussed later | Routed to a named owner |
Principle | — | Matters more than the tool |
The principle is more important than the tool. Your OKR system should make you aware of the deterioration before it's too late to change the plan.
Writing Engineering OKRs That Hold
Attach each KR to an outcome. If a KR is an activity ("migrate services"), rewrite it to describe the outcome ("migrate services enough that deployment time drops from 45 to less than 10 minutes").
Don't use pure activity metrics - "Ship feature X" is an output. "Feature X increases activation by 15%" is an outcome. If you can't explain the results, ask yourself if the feature is the right work.
Set baselines - You cannot set a target without knowing where you are. Measure the current state before the beginning of the quarter. If there is no baseline, then you are guessing.
Link to business outcomes - Make the connection clear – "Reduce p99 API latency from 840ms to 150ms" has an impact on product experience and conversion rate.
TABLE 12 · REWRITING ACTIVITY AS OUTCOME
Same work, different key result
Activity KR | Outcome KR | |
Migration | Migrate services | Deployment time 45 min → under 10 |
Feature work | Ship feature X | Feature X lifts activation 15% |
Performance | Optimise the API | p99 latency 840ms → 150ms |
Testing | Increase unit test coverage | Incidents caught before production |
Onboarding | Write onboarding docs | New hires ship in week one |
Long lists are replaced by fewer, clearer OKRs. Start with 2 objectives and 2 KRs each. If you have 8 objectives and 5 KRs per objective, no one is tracking them.
TABLE 13 · OKRS VS KPIS
Two instruments, two jobs
OKRs | KPIs | |
Time horizon | The quarter | The day |
Purpose | Make you better | Tell you if things are on track |
Expected attainment | 70% is a win | Near 100%, almost always |
Nature | North Star | Health monitor |
Recommended volume | 2 objectives, 2 KRs each | As many as you can act on |
FAQs
What are good engineering OKR examples?
Good engineering OKRs are focused on actual results, such as reducing MTTR from 38 minutes to less than 15, shipping code every day rather than every week, reducing the number of rollbacks from 11% to below 2%, or reducing build times from 25 minutes to less than 5. The key is picking targets that actually matter to your team and your users.
How are engineering OKRs different from KPIs?
Consider OKRs your North Star for the quarter – goals that are bold enough that 70% is a win. KPIs, however, are your health monitors for the day. You should be achieving those almost all the time. OKRs are designed to make you better; KPIs are designed to let you know if things are on track.
Should engineering OKRs use story points?
Don't. Story points are useful for planning sprint work, but they are a measure of effort, not impact. You're rewarding activity instead of results if you turn story points into a KR. Rather, monitor metrics such as how often you deploy, how long changes take to go live, your error budget, or your mean time to recovery – metrics that actually provide value.
What are examples of reliability OKRs?
Good reliable OKRs should aim for 99.95% uptime, a p99 latency of 150ms or less, an MTTR of under 15 minutes, or a P1 incident rate of less than 4 per quarter. All these are directly related to user experience and system trust.
How do you set OKRs for a platform team?
Platform teams should be about making life better for your engineers: faster builds, shorter onboarding, more teams using self-service tools, and fewer manual support tickets. Your task is to eliminate friction and speed up delivery, not to count how many migrations you've performed. Measure what hinders people, not what keeps you busy.
TABLE 14 · STORY POINTS AS A KR
Why effort should not become a target
Story points | DORA-style metrics | |
What they measure | Effort | Impact |
Legitimate use | Sprint planning | Quarterly outcomes |
What they reward | Activity | Results |
Gameable | Trivially | Harder — tied to production |
Examples | — | Deploy frequency, lead time, error budget, MTTR |
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