I handed my content marketing to agents. 6 months later organic traffic is 34x.

The scale is impressive, but the retarget queue sounds like a routing problem more than a writing problem.

I’d group the 2,418 pages using signals you already collect—topic cluster, keyword tier, article age, index status, first-query impressions, and SERP volatility. Review a small sample from each group, then give that group one route: target a reachable long-tail variant, consolidate into a stronger existing page, or retire it.

That keeps the final call human, while turning thousands of page-by-page decisions into a handful of repeatable policies. I’d also track whether each retargeted group earns impressions or clicks after 28–56 days, otherwise the queue will refill without teaching the system anything.

The deletion schedule is the part I want to steal. I am at the opposite end of the authority spectrum and it applies even harder there.

DA 3, ten referring domains all scrapers, four visits a month. Every page at zero impressions is actively diluting the crawl budget that might get one good page seen. You can afford a 1,601-page cull when thousands of others pull clicks. I would be cutting from maybe 40 pages, so the discipline has to start before publication.

Your April-to-June zero-impression drop, 53 to 25 percent, by moving the keyword tier down is the cleanest experiment I have seen on targeting versus quality. Same pipeline, same agents, same domain. Only variable is which queries you aimed at. That falsifies the objection that AI content is penalised, because the content did not change.

Does your 28-day retarget script check whether the page was crawled, or just whether it got impressions? Never fetched is a different problem from fetched-indexed-and-ignored.

The retarget queue is the interesting question, and there is a second axis you can sort it on that costs nothing to compute.

First split the 2,418 by impressions, because two different defects are sitting in one bucket. Zero impressions is a targeting miss and your existing tiering already fixes it. Impressions with zero clicks is a title and intent problem, and retargeting the keyword will not touch it.

Then sort the rest by whether the answer can be inlined. We measured this on two sites this month. One carried 87,400 Copilot citations over 90 days and zero sessions attributable to an AI source, because every top cited page was a review or a pricing comparison, which an answer engine reads out in full and the reader is finished. The pages that turned a citation into a visit were the ones whose value cannot be carried inside the answer: setup guides, live comparisons, anything with a tool or an input on the page.

So for 2,418 articles, rank by how inlineable the answer is and work the non-inlineable ones first. An inlineable page sitting at zero will still be at zero after you retarget it, because the click was never available to win. That also reads on your own numbers: 12.4 pages per person down to 1.9 is what one-answer-per-page looks like, and it is fine while ads are the monetisation, but it means every page has to earn its click on the way in rather than from the page next to it.

Caveat: two sites, neither publishing at your volume, so it is a lead and not a law.

The other thing your post surfaces and does not measure is the inbound. Link exchange requests from listed companies arriving daily, after years of none, is a channel you have opened and are not instrumenting. If you want a free read on the channels the blog is now earning attention on, contentmation.com/score runs a pass with no login. At your scale it will tell you nothing new about search, which is most of your traffic, so it is only worth the minute for the rest.

I'm testing And copy pasted my agent to see if it works. for my b2b

Two things stood out. First, "the tier you aim at decides the rank you get" is a cleaner way of saying what most SEOs learn the hard way: scaling output without tiering is just burning index budget. Your April to June pivot, from 53% zero-impression pages to actually ranking, is a great case study in that.

Second, the link exchange requests from listed companies feel like the real moat forming. The 34x traffic is the headline, but inbound interest from much bigger companies means the library is becoming a reference asset that compounds on its own. I'd track that inbound-request rate as its own KPI - it's a leading indicator that the content has crossed from "indexed" to "authoritative."

This is the measurement visibility problem at scale - you handed off the work but now have to measure what the system is actually doing. The 34x traffic number is powerful because it's observable, but most teams using agents for content never set up the instrumentation to catch what worked. Measuring agent outputs backwards from actual user behavior is the only way to know if you're running agents or they're running you.

The deletion system may be the most valuable part of this, but I’d keep a small control group before trusting the 28/56-day rules completely.

Leave perhaps 5–10% of zero-impression pages untouched for another 60 or 90 days, grouped by keyword tier and index status. Then you can see how many would have surfaced naturally versus how many remained dead.

Without that holdout, pruning and growth happen together, so it is difficult to separate the value of deletion from the improved keyword targeting. It would also tell you whether different tiers deserve different expiry windows.

The 2,418-page retarget queue is more interesting than the 34x traffic. How do you decide which pages deserve another target versus deletion?