Why reducing Mean Time to Resolution matters more than preventing every bug

Production issues are inevitable. Systems fail, bugs slip through reviews, and outages happen even in well-run teams.

What matters is how quickly teams respond. The faster you move from alert to fix, the smaller the impact on users and the business.

Mean Time to Resolution (MTTR) measures this speed. It tracks how long it takes to detect an issue, diagnose the cause, ship a fix, and restore normal operation. Recent data shows many teams still struggle here. According to Palo Alto Networks’ State of Cloud Security Report 2025, nearly one in three organizations takes more than a day to fully close an incident, and 9% take a week or longer.

Detection is only the first step. Teams still need to understand what broke, fix it safely, and deploy under pressure. Delays at any stage extend downtime and increase risk

TLDR

• Mean Time to Resolution (MTTR) measures how long it takes to fix production issues from detection to deployment. Research shows that in nearly 1 in 5 security cases, data exfiltration happens within the first hour of compromise, making a fast response critical. 

• Reducing MTTR requires faster detection through automated monitoring, quicker diagnosis with better logging and context, rapid fixes using AI coding assistants and automated code review, and streamlined deployment through CI/CD automation. 

• AI review tools like cubic continuously analyze repositories to catch issues before they reach production. The combination of proactive scanning and reactive incident response creates systems that both prevent issues and recover quickly when problems occur.

What slows down production fixes

When production issues take too long to fix, the delays usually come from the same few steps. These are the areas that slow teams down most: 

1. Delayed detection

Issues often run in production before anyone notices. Alerts fire but get ignored, or user reports take time to reach engineers. Every delay in detection extends the impact on users.

2. Difficult diagnosis

Finding the root cause usually takes longer than fixing it. Logs are incomplete, errors lack context, and the engineer investigating the issue may not know the code well. Reproducing the problem adds more time.

3. Slow code fixes

Even after diagnosis, fixes aren’t always quick. Business logic can be complex, tests are required, and multiple services may need changes. Working under pressure increases the risk of mistakes.

4. Deployment delays

Fixes don’t help until they reach production. Release windows, service dependencies, and approvals can slow deployment, extending resolution time even when the code is ready.

How to detect production issues faster

Reducing detection time directly lowers MTTR. The earlier a problem is identified, the sooner teams can begin diagnosing and fixing it.

These approaches focus on improving visibility and shortening the gap between failure and awareness: 

1. Automated monitoring and alerting

Manual checks don’t scale. Automated monitoring continuously tracks error rates, latency, resource usage, and key business metrics to surface anomalies early.

Alerts must be carefully tuned; too many create noise, too few allow issues to slip through. The goal is high-signal alerts that indicate real problems.

2. User-reported issues

Users often experience issues before monitoring systems detect them. Make it easy for users to report problems directly to engineering teams.

Including error IDs in user-facing messages allows engineers to quickly locate relevant logs and diagnose issues without back-and-forth.

3. Continuous codebase scanning

Some issues exist in code before they cause visible problems. Continuous scanning finds these issues proactively.

cubic's automated codebase scans run thousands of AI agents across entire repositories to find bugs, security vulnerabilities, and code quality issues. This catches problems before they reach production, reducing the number of incidents that need reactive fixes.

How to speed up code fixes with AI assistance

Writing fixes faster without introducing new bugs requires tools that understand code context.

1. AI coding assistants

Tools that suggest code completions and fixes speed up the writing process. They generate boilerplate, suggest error handling patterns, and write tests based on existing code.

AI coding assistants integrated into development environments provide immediate help while writing fixes, reducing the time from diagnosis to deployable code.

2. Automated code review during fixes

Code review catches issues before they reach production. Even fixes need review to prevent introducing new bugs.

Purpose-built code review platforms analyze code automatically, flagging potential issues in fixes before human reviewers see them. This reduces review time while maintaining quality.

3. Pre-tested fix patterns

Common issues have common fixes. Maintaining a library of tested fix patterns helps engineers solve familiar problems quickly.

When error X occurs, apply pattern Y. The fix is already written and tested. Engineers just need to adapt it to the specific context.

Streamlining deployment

Getting fixes to production quickly requires automated, reliable deployment processes.

1. Automated CI/CD pipelines

Manual deployment steps slow down fixes. Automated pipelines run tests, build artifacts, and deploy to production without manual intervention.

This reduces deployment time from hours to minutes. Engineers merge fixes, and pipelines handle the rest.

Learn more about how to integrate AI Code Review into existing CI/CD Pipelines. 

2. Feature flags for quick rollbacks

Sometimes fixes don't work as expected in production. Feature flags allow instant rollback without redeploying.

Enable the fix behind a flag. If issues appear, disable the flag immediately. This makes trying fixes safer because reverting is fast.

3. Deployment frequency

Teams that deploy frequently deploy faster. The deployment process is well-practiced and automated. Infrastructure is designed for safe, quick deployments.

Infrequent deployers have manual steps, approval processes, and deployment anxiety. This makes emergency fixes slower because the deployment mechanism itself is risky.

How do you measure and improve MTTR?

You can't improve what you don't measure. Track MTTR to identify where improvements have the biggest impact.

1. Calculate MTTR properly

MTTR is the total resolution time divided by the number of incidents. But the details matter.

When does the clock start? When the issue occurs, when monitoring detects it, or when an engineer acknowledges it? When does it stop? When the fix deploys, or when the issue is verified as resolved?

Define these clearly so measurements stay consistent over time.

2. Segment by severity

Not all incidents are equal. Critical outages should resolve faster than minor bugs. Calculate MTTR separately for different severity levels.

This shows whether your team prioritizes appropriately and whether critical issues get the fast response they need.

3. Track trends over time

MTTR this month, compared to last month, shows whether processes are improving. Large changes indicate either process improvements or degradation that needs attention.

The role of proactive scanning

Fixing production issues faster is valuable. Preventing them from reaching production is better.

Automated codebase scanning finds issues before deployment. This reduces the number of production incidents that need reactive fixes.

cubic scans entire repositories continuously, catching bugs in older code, security vulnerabilities from compromised dependencies, and architectural problems that span multiple files.

When cubic finds issues, it automatically notifies the developer who introduced them. Developers can fix problems with one click using background agents or integrate fixes into their normal workflow.

This proactive approach works alongside reactive incident response. Catch what you can before production. Fix what slips through as fast as possible.

Building systems that recover quickly with AI code review

Production issues will happen. What matters is how quickly teams can detect, understand, and fix them.

AI code review plays a key role in reducing recovery time across the full incident lifecycle. It supports faster detection by catching risky patterns early, improves diagnosis by providing architectural and code-level context, and speeds up fixes by suggesting changes that align with the existing codebase. 

When AI code review is integrated into the CI/CD pipeline, teams catch risky changes earlier and move through fixes faster. Engineers spend less time tracing what broke and more time shipping the fix. 

Want to see how AI code review can shorten recovery time?

Book a demo to see how cubic catches issues before production and helps teams fix problems faster when they occur.