Most engineering OKRs fail twice: they don't ladder to anything the org actually wants, and they get gamed the moment they become targets. This playbook drafts OKRs with AI, then turns the model against them — an alignment check, a measurability critique, and a Goodhart red-team that pairs every key result with its counter-metric.
Workflow area: Delivery & Reporting · Tier: Core · Hands-on time: ~30 minutes
Goodhart's law — when a measure becomes a target, it ceases to be a good measure — is not a risk in engineering OKRs; it is the default outcome. "Reduce incident count" teaches teams not to declare incidents. "Increase deploy frequency" produces confetti deploys. AI is genuinely good at drafting crisp OKRs, and even better at something leaders rarely ask it for: playing the smart, cynical team that will game them. That adversarial pass is what this playbook adds to a chore you already do quarterly.
Assemble the inputs. You need three things: the org- or business-level goals for the period (verbatim, not your paraphrase), your team's mission and current commitments, and last cycle's OKRs with their actual outcomes. Ten minutes of collection determines the quality of everything downstream.
Draft candidate OKRs with alignment stated explicitly.
Draft OKRs for my team for [PERIOD].
Org goals (verbatim): [PASTE]
Team mission and context: [PASTE]
Last cycle's OKRs and outcomes: [PASTE]
Produce 2–3 Objectives, each with 2–4 Key Results. For every KR:
- the metric, its current baseline (write [BASELINE NEEDED] if
I gave you none), and the target
- WHERE it is measured (which system produces the number) and
WHO owns reporting it
- one line tracing it to a specific org goal above. If you
cannot trace it, mark [ALIGNMENT GAP] — do not invent a link.
Outcomes over output: no KR may count activity (tickets closed,
PRs merged) unless the activity is the business outcome.
Run the measurability critique. Follow up in the same conversation: "Critique each KR as a measurement: is the number observable in a named system today, is the baseline real, can we tell success from failure at quarter end without argument? Flag any KR that needs a new dashboard to exist." Kill or fix anything that fails — a KR nobody can measure by week 3 becomes a KR everybody ignores by week 6.
Red-team for Goodhart, then pair counter-metrics.
Now act as a smart, cynical team that will be graded on these
KRs. For each one:
1. GAMING VECTOR — the easiest way to hit the number while
betraying the objective's intent.
2. COUNTER-METRIC — one paired metric that gets worse when the
KR is gamed, measured in a named system.
3. Verdict: SAFE (hard to game), PAIR (needs its counter-metric
reported alongside), or REDESIGN (invites the wrong behavior).
Good output looks like:
KR: Reduce p1 incident count 30%
GAMING VECTOR: reclassify p1s as p2s; discourage declaring.
COUNTER-METRIC: customer-reported issues + mean severity of
declared incidents (PagerDuty + support tickets).
Verdict: PAIR — report both numbers together, always.
KR: Cut PR review turnaround to <4h
GAMING VECTOR: rubber-stamp approvals, review depth collapses.
COUNTER-METRIC: post-merge defect escapes + revert rate.
Verdict: PAIR
Human pass: cut to what you'll actually drive. The model happily produces five objectives; a team drives two or three well. Cut, sanity-check every target against baselines yourself (models cheerfully propose "improve 30%" against baselines they never saw), and confirm each counter-metric will genuinely be reported alongside its KR — a counter-metric nobody publishes is a design document, not a guardrail.
Publish the pairs, not just the KRs. Wherever the OKRs live, the counter-metric appears in the same row as its KR, and every review of the KR reviews both numbers. Announcing the gaming vectors to the team is optional but powerful: it says you know how the metric bends, and you're watching the pair.
[ALIGNMENT GAP] means you're about to spend a quarter on something the org didn't ask for; that's a conversation, not a KR.OKR drafts often reference unannounced product plans, revenue figures, and client names — keep drafting inside your approved AI tenant, and never paste another team's performance data into the gaming critique without their knowledge.
Draft the OKRs with AI, then make the same model attack them — a key result published without its counter-metric is an instruction to your smartest people to game it, and they will follow that instruction precisely.