Two years ago, a full technical + content audit for a mid-size e-commerce site took me a full week. Today the same depth of audit takes an afternoon, and the report is more consistent than what I used to produce by hand. The difference isn't a single tool — it's a workflow built around AI agents that handle the repetitive parts while I stay in charge of judgment calls.
Why I automated my SEO audits
Most SEO work is pattern-matching against a checklist: crawl for broken links, check title/meta uniqueness, flag thin content, cross-reference Search Console queries against rankings. None of that requires creativity — it requires patience and consistency, which is exactly what LLM agents are good at when you give them the right context and constraints.
The goal was never to remove myself from the process — it was to remove the parts of the process that didn’t need me.
The stack I use
Nothing exotic — the value is in how the pieces are wired together, not the tools themselves:
- A crawler script that exports every URL, status code, and metadata field to a structured file
- An LLM agent that reads the crawl output plus Search Console data and drafts prioritized findings
- A second pass agent that checks the first agent's claims against the raw data before anything reaches a client report
The workflow, step by step
Every audit follows the same four stages: crawl, draft, verify, review. The verify stage is the one people usually skip when they first automate — and it’s the one that determines whether you can trust the output. I never ship a finding an agent produced without a citation back to the raw data it came from.
Lessons learned
- Agents are excellent at breadth (checking 10,000 URLs) and weak at judgment (deciding what actually matters to a business)
- Give agents a narrow, well-defined task each — a single agent trying to "do SEO" underperforms three agents each doing one thing well
- Every automated claim needs a way to trace it back to source data, or it isn't trustworthy enough to act on
