如何像工程師一樣評估 AI SEO 智能體

"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.

Minimum definition

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.

Five evaluation questions

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.

Evidence to demand in a trial

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

For research assistants:

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