Running a Postmortem After a Failed Launch

Postmortems for failed launches build the next launch. Skip them and you'll repeat them.

Within 2 weeks

Schedule the postmortem 1-2 weeks after launch. Long enough to recover; short enough to remember.

Timeline before analysis

First 30 minutes of the meeting: just facts, in order. No interpretation. Then analysis with the timeline in hand.

Three categories of fix

Code fixes, process fixes, communication fixes. Each gets a tracked ticket and an owner. Without ownership, postmortems are theater.

Share with the org

Publish the postmortem. Not the names; the lessons. Other teams shouldn't have to repeat the discovery.

Understanding the issue

AI bugs are emergent. The code is correct in isolation; the behavior emerges from interaction with other systems. Reproducing means controlling the interaction; fixing means deciding which interaction was wrong.

Operational practices like this one tend to be most valuable when adopted before they're obviously needed. Studios that wait until a crisis to implement quality controls find themselves implementing under pressure, with less time to design well and more pressure to ship features. The practice ends up shaped by the crisis rather than by what would have worked best.

Why this matters

Process bugs are slower to surface than code bugs because they don't fail loudly. A team that handles bug reports poorly accumulates a backlog quietly; a team with the wrong triage taxonomy slowly loses the signal to noise ratio in their tracker. The cost compounds without being visible until something else exposes it.

The practice described here has both an obvious benefit (the one in the title) and several non-obvious ones. Teams that adopt it usually notice the obvious benefit first; the non-obvious benefits surface over time as the practice composes with other team habits. This is part of why adoption is hard - the upfront benefit isn't always commensurate with the upfront cost, but the long-term return is.

Putting it into practice

Measuring whether this practice is working requires honest data, not aspirational metrics. Pick a number that actually moves when the practice is followed (cycle time, fix rate, error count) and not one that moves with general activity (total commits, total bugs filed). The first kind tells you the practice is working; the second kind just tells you the team is busy.

Adopting a practice without measurement is faith-based engineering. Measurement makes it data-driven. The first metric you pick will be wrong; that's fine. Use it for a quarter, see what it actually tells you, refine. The third or fourth iteration of the metric is when it starts to be useful.

Adapting to your context

Specific industries (mobile, console, VR, multiplayer) have their own variations on this practice. The core idea is portable; the implementation depends on the platform's constraints. Borrow from teams in your space.

Tailor this practice to your context rather than copying verbatim from another team's implementation. What's appropriate for a multiplayer-focused studio differs from what's appropriate for a narrative-focused one. The principles transfer; the specifics don't.

Long-term maintenance

When this kind of process is missing from a studio, the gap is usually invisible until someone points it out. The team that didn't realize their cycle time was 14 days finds out when they hire from a studio where it was 3. Benchmarks matter - keep some external reference for your own quality bars.

The hardest part of operational changes isn't the change - it's the ongoing maintenance. Build the maintenance into existing rhythms: a quarterly retrospective, a monthly review, a weekly check. The cadence matters because human attention drifts; structure replaces willpower with habit.

Throughput considerations

Measure the throughput cost of new practices honestly. If you add a step to triage, that step has a per-bug cost. The cost is acceptable when the practice surfaces signal worth the cost; otherwise it becomes friction.

How to start

Before changing how your team works, gather baseline data on the current state. Without baselines, you can't tell whether your change made things better, worse, or simply different. Even rough measurements - 'we close about 20 bugs per week, sev-1 takes about 3 days' - are valuable as starting points for comparison.

Pilot the change with a single team or a single feature before rolling it out broadly. The pilot teaches you what implementation details actually matter; the broad rollout applies what you learned. Skipping the pilot means you discover the gotchas during the rollout, which is too late to redesign the practice.

Supporting tooling

The tooling that supports this practice has a multiplicative effect. A team with a custom dashboard for the relevant metrics moves faster than a team that calculates them by hand each time. The cost of building the dashboard is paid back in months; the value is the persistent visibility it provides.

When evaluating tools to support this practice, prefer ones that integrate with what your team already uses. A purpose-built tool may have better features, but adoption depends on the team using it consistently. The integrated tool that's used 95% of the time usually beats the best-in-class tool that's used 60% of the time.

Adoption pitfalls

Adoption pitfalls vary by team. Small teams struggle with overhead; large teams struggle with consistency; distributed teams struggle with communication. Anticipate the pitfall most likely to affect your team and design around it from the start.

Watch for the pattern where the practice 'almost' works - everyone says they're following it, but the metrics don't move. This is the most common failure mode: surface compliance without underlying behavior change. The fix isn't more documentation; it's making the practice's effect visible through tooling or rituals.

Communicating the change

Onboarding new engineers to this practice takes deliberate time. Documentation is a starting point; pairing on a representative example is what makes it concrete. Budget time for the second step; without it, new engineers approximate the practice instead of doing it.

Communicating the practice externally - to candidates, to other studios, to the broader industry - reinforces it internally. Teams that talk publicly about how they work tend to do that work better. The act of explaining clarifies the practice for the team, and the external audience holds the team accountable to the public version.

“Failed launches teach. The teaching is wasted if you don't capture it.”

Make 'no blame' a meeting ground rule, repeated at the start. Engineers will name themselves; the rule prevents others from naming them.