SEO 智能體找到優質關鍵字，不代表建議就值得採納

A keyword can have high search volume, low competition, and still be a bad choice for your website.

That's one problem I want to solve when building an SEO agent.

It's easy to ask an agent to find keywords, check search volume, look at competitors, and return a list of opportunities. The harder part is making it question its own result before handing them over.

Finding keywords is only half the job

Imagine an SEO agent finds a keyword with 5,000 monthly searches and a low difficulty score.

It looks promising. But the keyword might attract people who will never use your product. Or the search results might be full of guides when your agent recommends creating a product page.

The numbers look good, but the recommendation doesn't.

This is why I wouldn't build an SEO agent that simply collects metrics and ranks keywords by opportunity score.

I'd want it to explain why a keyword is worth targeting, then check whether that reasoning holds up.

Give the agent a second pass

One way to approach this is to split the work into two stages.

The first stage finds potential keywords. It looks at search data, competitor rankings, related terms, and the pages already on your website.

The second stage tries to find reasons not to recommend them.

For each keyword, it could ask a few simple questions:

• Does the search match what the business offers?
• What are the top-ranking pages actually about?
• Is the keyword different enough from topics we already cover?
• Is there evidence that competitors are getting traffic from this topic?

What information is missing before we can make a confident recommendation?

The second pass shouldn't exist just to agree with the first one. It should be allowed to reject a keyword, lower its priority, or ask for more data.

For example, a keyword might have decent search volume but the wrong intent. Another might have low volume but closely match a problem the target audience is trying to solve.

The agent should be able to explain that difference instead of treating search volume as the deciding factor.

Make the reasoning checkable

I'd also avoid asking the agent to produce a single score without showing how it got there.

A useful recommendation should include the keyword, the intended audience, the reason it matters, the evidence behind it, and the main risk.

That makes it easier to spot weak reasoning.

If the agent says a keyword is a good opportunity because competitors rank for it, that isn't enough on its own. Are those competitors relevant? Are they ranking with pages that match the same search intent? Does the topic fit the website?

These are questions the agent should investigate rather than answer from guesswork.

There's also a difference between missing evidence and negative evidence. If search data isn't available, the agent shouldn't pretend the keyword is a poor choice. It should say that it doesn't have enough information to judge.

That distinction matters if the output is going to guide real content decisions.

Don't let the agent invent certainty

There's a limit to how much a second pass can do.

If both stages rely on the same incomplete data, the second stage can repeat the same mistake. Asking an agent to critique its own answer doesn't automatically make the answer correct.

I'd use independent evidence where possible, such as search results, keyword metrics, competitor pages, and the site's own Search Console data. I'd also test recommendations against examples that have already been reviewed by a person.

The goal isn't to make the agent sound more confident. It's to catch bad recommendations before they reach the content plan.

For a workflow like this, an SEO research tool such as Inteldo's SEO Analyst is one example of where competitor and site data can inform a broader research question. The key design question is still how the workflow checks its conclusions and shows the evidence behind them.

What I'd measure

I wouldn't judge the agent only by how many keywords it finds.

I'd want to know how often its recommendations are judged relevant by a human reviewer, how often it misses useful opportunities, and how often its reasoning relies on missing or weak evidence.

I'd also track how many recommendations get rejected and why. A high rejection rate might mean the first stage needs work. A low rejection rate isn't necessarily good if the reviewer is simply approving everything.

For me, the important part of an SEO agent isn't how many keywords it can return.

It's whether it can explain why a keyword matters, identify what could make that recommendation wrong, and change its answer when the evidence doesn't support it.

Finding keywords is straightforward. Making better decisions with them is the harder engineering problem.