Deploy-Induced Regression: The Most Common Incident Your Team Is Causing Itself

If you want to find the most common cause of service incidents, look at what deployed 30 minutes ago.

Deploy-induced regression (FM-09) is the second most frequent failure mode in the SSOR 2026 dataset at 19% of classified unplanned incidents. It's also the most fixable at scale: the detection pattern is consistent, the remediation is well-understood, and the autonomy potential for AI SRE tooling is the highest of any failure mode in the taxonomy.

We analyzed 178,000+ status page incidents and 1,037 engineering post-mortems. Full data: stackgen.com/state-of-reliability.

The Consistent Fingerprint

FM-09 has a three-step fingerprint that is entirely automatable from telemetry:

1. Incident starts within 30 minutes of a deploy
2. Deploy log correlates with error spike
3. Rollback resolves it

No business-logic judgment required.

The Data: 3,764 Incidents in 2025

Anthropic: 73% of classified incidents map to application errors on specific model versions \u2014 \u201cElevated errors on Claude Opus 4.6,\u201d \u201cIncreased errors on Sonnet 4.6.\u201d Each one is a deploy-correlated regression on a named model, resolved by routing back to the prior version.

OpenAI: 49% of classified incidents trace to application errors with a growing MTTR trend (2023\u20192026 median +93%) \u2014 workload complexity outpacing rollback discipline.

Why MTTR Varies So Much

Deploy regression MTTR ranges from under 15 minutes to over 8 hours for structurally similar incidents. The variance comes down to three things:

1. Time to correlate the deploy: fastest teams have automatic deploy-to-incident correlation. Others spend 30\u201390 minutes establishing what changed.
2. Whether rollback is a one-step operation: one-click rollback vs. full CI/CD pipeline re-deploy = 20\u201345 minutes difference.
3. Canary discipline: teams that deploy to a percentage of traffic first and have automated health gates catch regressions before they become public incidents.

The AI-Specific Variant

Model deploy regressions (FM-17) follow the same operational mechanics as code deploy regressions (FM-09). Canary discipline applies to model deployments too: evaluate on a traffic sample before full rollout. Teams that treat model version upgrades with the same discipline as application deploys show lower FM-17 rates.

The Remediation Playbook

1. Correlate automatically: error spike + deploy log + time delta
2. Rollback immediately: don't debug \u2014 rollback first, debug later
3. Confirm via health gate: verify rollback resolved before closing
4. Blameless retrospective on the rollout process: the code defect is RC-01; the process question is why it passed staging

The single biggest MTTR improvement: moving from \u201cdebug then fix forward\u201d to \u201crollback first, debug later.\u201d In the post-mortem corpus, this pattern adds an average of 90+ minutes to FM-09 MTTR when rollback was ultimately the resolution anyway.

Key Takeaways

• 19% of 2025 incidents are deploy regressions \u2014 entirely within your control
• The fingerprint is consistent: incident starts within 30 minutes of deploy, rollback resolves. Highest-automation-value pattern in the taxonomy.
• Rollback discipline is the lever \u2014 not incident complexity
• Model deploys need the same canary discipline as code deploys

stackgen.com/state-of-reliability | LinkedIn webinar