OKR Examples · 9 min read

Engineering OKRs: Real Examples Across Platform, Velocity, and Security

Engineering teams have a complicated relationship with OKRs. Most engineers are trained to think in deliverables — features shipped, bugs closed, PRs merged — and translating that into outcome-based OKRs requires a different mental model. The good news is that engineering generates some of the most measurable data in the company. The hard part is deciding which metrics actually matter and setting targets that are ambitious without being fabricated.

Published May 2026 · OKIAR Editorial

Why "features shipped" is the wrong KR

The most common engineering OKR mistake is measuring output — features shipped, story points completed, PRs merged — rather than the impact of that output. Shipping 47 features in a quarter and having zero impact on user retention is a failure, not a success. The OKR framework is asking: what changed in the world as a result of engineering's work?

That said, some engineering work is inherently output-oriented — infrastructure, reliability, security. The examples below include cases where the output itself is the outcome (e.g., "zero P0 incidents lasting over 15 minutes") alongside cases where impact is the right measure.

Platform reliability OKRs

Note the specificity. "99.95%" is a different conversation than "improve uptime." It means 22 minutes of unplanned downtime per month maximum. Teams that know that number plan very differently from teams that want to "be more reliable."

Developer velocity OKRs

Developer velocity OKRs are powerful because the impact is both internal (engineering teams move faster) and external (product ships more frequently). The specific numbers here — 3h 40min → 48min — came from actual measurement. Teams that don't instrument their pipelines can't write KRs like this, which is itself an argument for doing the instrumentation first.

Tech debt OKRs

Tech debt is notoriously hard to write OKRs for because the "outcome" is usually framed as risk reduction — a negative. A better framing: what becomes possible once the debt is paid?

KR2 here ("launch time: 6 weeks → 5 days") is the business outcome that justifies the tech debt work. It's the answer to the CFO's question: "Why are we spending 8 weeks on refactoring instead of shipping features?"

Security OKRs

Product engineering OKRs (feature impact)

This is how engineering OKRs should look when the work involves user-facing features. KR1 measures adoption, not just launch. KR2 measures whether the feature improves the metric that drives the business. KR3 captures user satisfaction. None of these measure "did we ship it" — they measure whether shipping it mattered.

How to set baseline numbers

The single biggest blocker to good engineering OKRs is missing baselines. Teams can't write "P95 response time from 420ms to 150ms" if they don't have monitoring that shows P95 response time. Before setting OKRs, instrument the metrics you want to move. Two weeks of measurement data is usually enough to set reasonable targets.

Target-setting: use the rule of 1.5–2x improvement for reliability and performance KRs when you have runway to do the work. "We're at 420ms and the industry standard is 100ms" gives you an anchor. Splitting the gap in one quarter (to 150ms) is aggressive but credible. Claiming you'll hit 100ms in 12 weeks without a specific plan is a stretch goal pretending to be a KR.