AIでルーティンワークを自動化した話

The Problem

Every DevOps engineer has a list of “things I should automate but haven’t gotten around to.”

Mine looked like this:

1. Log analysis when incidents happen
2. Code review for security issues
3. Writing documentation for infrastructure changes
4. Responding to common production alerts
5. Drafting incident postmortems

These tasks were:

Repetitive: Same pattern every time
Time-consuming: Hours per week
Mind-numbing: No creative thinking required

The perfect automation targets. So I automated them with AI.

What I Built

1. Incident Log Analyzer

Before:

# Me, at 2 AM, tired:
grep -i "error" production.log | head -100
# ...staring at output, trying to find patterns

After:

$ ai analyze-logs production.log --incident "payment-failure"
> Analyzing 47,892 log entries...
> Identified root cause: Database connection pool exhausted (95% utilized)
> Recommendation: Increase pool size from 100 to 200
> Confidence: 94%

The AI reads the logs, identifies patterns, and suggests fixes. I still make the decision, but I spend 10 minutes instead of 2 hours.

2. Automated Code Review

I added a GitHub Action that runs on every PR:

- name: AI Security Review
  uses: anthropic/claude-code-action@v1
  with:
    task: security-review
    focus: authentication, authorization, injection vulnerabilities

It catches about 80% of the security issues that used to slip through. The remaining 20%? Still needs human judgment.

3. Infrastructure Documentation

Terraform files are notoriously poorly documented. Now:

$ ai doc terraform/modules/networking/
> Generated documentation for 12 resources
> Added usage examples
> Identified 3 potential cost optimizations

What Didn’t Work

Pure decision-making automation. AI can suggest, but shouldn’t decide on:

Production deployments
Security exceptions
Rollback triggers

Complex debugging. AI struggles with distributed systems bugs that require understanding of multiple services simultaneously.

The Results

After 6 months:

Task	Before	After	Time Saved
Log analysis	2 hours	15 min	87%
Code review	1 hour	20 min	67%
Documentation	45 min	10 min	78%
Postmortems	1 hour	15 min	75%

Total: ~8 hours/week recovered.

The Point

AI automation isn’t about replacing DevOps engineers. It’s about removing the boring parts so we can focus on interesting problems.

The goal isn’t to work less. It’s to work on things that actually matter.