How to evaluate an "AI SEO agent" like an engineer would

"Agent" now covers everything from a chat assistant with SEO data access to systems that deploy changes to production websites. If you're evaluating these for your team or a client, treat it like any other system with write access.

For my comparison of 10 tools, an agent had to pursue an SEO task across multiple steps using data or tools, not just generate a paragraph or render a chart. Execution can still be gated by approvals, integrations, or plan tier.

yaml ownership: analyze | prepare | execute | multiple grounding: first_party_data | crawl_evidence | third_party_research execution: what can it change? via which integration? control: inspect | approve | restrict | rollback verification: proof of change + outcome measurement MCP is not an agent

Worth stating clearly because it trips people up: MCP connects an assistant to supported tools. Data access through MCP and permission to act in another system are separate decisions. Also check whether every web-app action is actually exposed via MCP. Often it isn't, and release scopes differ between the platform UI and the MCP interface.

For deployment tools (e.g., OTTO, Alli AI):

original_value, deployed_value, affected_url, rendered_result, rollback_steps

For content tools (e.g., Frase, Writesonic, Keytomic):

brief, sourced_draft, editor_revision, cms_output, list_of_steps_still_manual

query, source_data, filters, recommendation, which_tool_produced_each_number, reproducible? Pilot design

Same task, same inputs, same approval boundaries for every finalist. Don't force a deployment tool into an article-writing test. Log editor time including corrections, not just generation time.

Full comparison with sources and pricing: https://keytomic.com/blog/ai-seo-agents