Google’s AI Agents Are Talking to Themselves in Your Search Console

A machine prompt is leaking into query reports across at least 20 unrelated accounts, in at least two languages. It inflates impressions, wrecks CTR, and shows up in client reports. Here is what the string means and the filter that removes it.

Published September 6, 2026 · On-Page.ai Blog

In the first week of September, an SEO posting on Reddit under the handle Strong-Ad-2167 asked a question the community could not answer. The same strange query kept appearing in Search Console performance reports, account after account:

context: location: united states (not for language). do not include location references in your response. question:

The poster could verify the string across more than 20 unrelated GSC accounts. “We know it’s not human obviously,” they wrote in the r/bigseo thread on September 1, 2026, cross-posted the same day to r/SEO, “but can’t seem to figure out if it’s an agent/assistant/scrapper or something else.”

As of publication, Google has not publicly explained it, and none of the major search publications has covered it. But the string is legible if you read it as what it is. It is not a search. It is a set of instructions one machine gives another, with a slot at the end where a real person’s question gets inserted. The most supported reading of the evidence: this is Google’s own agentic search infrastructure wrapping a user’s question before it runs a grounding search, and the entire wrapper is leaking into your query log.

This piece dissects the string, explains how it reaches your report, sorts it into the taxonomy of leaked AI queries, quantifies what that class of traffic does to your metrics using three published datasets, and ends with a copy-ready filter playbook.

The string is a job ticket, dissected

Read the leak as four components. Each one does something specific, and together they describe a system built to ground AI answers in location-aware web search while hiding the location handling from the person asking.

Three things stand out once it is broken down this way.

The template is parameterized by country. A Japanese SEO blogger known as Kimagure Weblog independently documented the same scaffold on an outdoor-craft site’s Search Console, with the location swapped: context: location: japan (not for language)... followed by a real Japanese question about outdoor gear trends. Their write-up, based on a 90-day query filter, found two such rows, both attributed to the same article. The same wrapper with a LinkedIn-circulated US variant means one system serving multiple markets, not one site being probed.

The human question is still in there. The tail of every row holds what a real person asked, appended after question:. In the Japanese case: “I want to know the latest trends in outdoor gear.” A version shared on LinkedIn by Xenofon Tsimpogiannis continues into an English question starting with “how”. These rows are half machine instruction and half genuine demand, and almost everyone looking at them is discarding both halves.

The instructions are addressed to a model, not a search box. “(not for language)” and “do not include location references in your response” are generation constraints. No human types output-formatting orders into Google. That framing, retrieving with location while keeping location invisible in the answer, is a product decision, and it belongs to the system generating the answer.

How Google’s instructions end up in your query report

The mechanism has two documented parts and one inference, and it is worth keeping those separate.

• Documented: AI data lands in the ordinary performance report. Google’s own announcement of the Generative AI reports states the AI data “is included in the overall performance report,” which is the one you have been reading for years.
• Documented: follow-ups count as new queries, and links inside AI answers count as impressions. Google’s John Mueller has explained that an impression registers when a link to your page is shown inside an AI Overview or AI Mode response, and that follow-up questions in AI Mode are treated as new searches with everything in the response attributed to them.
• The inference: the context: location wrapper is the platform’s own grounding scaffold. It is not something a person typed, and it is not a third-party agent’s template of the kind Suganthan Mohanadasan documented. It carries product-level location handling of the sort AI Mode itself performs.

One structural fact rules out the most popular guess in the Reddit thread, that this is a scraper. A scraper or an external agent hitting your site directly shows up in your server logs. It cannot create a query row in Search Console, because query rows only exist for searches issued inside Google’s stack. The top reply in the thread, from a user called Slow-Commercial4316, made exactly this argument: whatever produces these rows “sits on their side.” A third party could only appear in your query report by calling Google’s own AI tooling, and even then the wrapper instructions would belong to the calling product. The combination of 20-plus unrelated accounts, two country variants, and location-versus-language handling points to Google’s pipeline.

The eighth kind of leaked query

On August 13, Suganthan Mohanadasan published a classifier for the AI conversations leaking into Search Console, built from 1,127 non-search queries and 20,300 impressions across sixteen months on his own property. He sorted them into seven kinds: reply artefacts, pivot follow-ups, conversational questions, tracker probes, agent harnesses, pasted strings, and a review pile. The new string belongs in that taxonomy as an eighth kind, distinct from the agent harnesses he already had. His harnesses were third-party prompt templates, like an engineer’s hallucination checker that ran roughly 2,160 times across five days in July. This one is the platform’s own, with location injection built in.

The classification ladder works because each kind has a tell. Suganthan’s tests look at who the query is addressed to: bare replies, “what about” pivots, pronouns with no referent, politeness markers. The platform harness gets caught earlier and more cheaply, by its prefix. Every observed instance begins with context: location:, a prefix no human search has any reason to start with.

What the machine layer does to your numbers

Three published datasets describe the damage, and they agree on the shape of it: machine rows carry impressions, hold strong positions, and almost never click.

On the Japanese property, both harness rows earned answer-block positions, averaging 2.1 on one, from eight impressions, ranks no typed search for that scaffold would ever hold. On Suganthan’s property, agent harnesses accumulated 2,181 impressions with barely any clicks despite good average positions, and the conversational bucket that surrounds them ran 8,834 impressions against 13 clicks. “That ratio is what being read inside answers looks like,” he wrote. Call Force Global, a B2B operator publishing its own Search Console data, measured the aggregate effect: queries matching machine patterns delivered a 0.04% CTR at average position 4.0.

The second finding from Call Force is the one that should worry anyone building a Q4 plan. Between two overlapping 30-day windows, on the same site with an unchanged classifier, the machine share of query-attributed impressions fell from 39.0% to 19.45%. Nothing changed on their side; one synthetic cluster simply ran less.

The implication for reporting: if your agency applied a one-time “bot adjustment” in July, your September numbers are wrong in the other direction. The human-versus-machine split has to be recomputed for every reporting window, because the synthetic traffic comes and goes with whoever is running probes and agents against your topics.

There is also a floor problem. Google anonymizes rare queries, and conversations are rare by definition, so most of the AI traffic never appears as a query string at all. In Suganthan’s BigQuery export, 57.7% of web impressions over 59 days carried no query string: 454,720 anonymized impressions against 333,651 visible ones. The leaked strings are the visible tip.

The new AI report will not save you

The obvious fix would seem to be Google’s Generative AI performance reports, which reached all websites worldwide on August 31 after a rollout that began in June. The reports separate your AI visibility into a dedicated view. They just happen to hide the two fields you need to detect and remove this traffic.

So the detection and cleanup happens where the leak happens: in the ordinary performance report and its exports. Which is also where client reports are built, and where the machine rows are currently being billed as organic visibility.

The filter playbook

Everything below works on a standard GSC query export. Run the platform-harness filter first, because its prefix match is exact and cannot eat a real query.

1. Check it in the Search Console UI

Performance, Search results, add a query filter, “containing” the exact text context: location:. If rows come back, you are affected. For Suganthan’s bare reply artefacts, his one-line custom regex works in the same filter: ^(yes|yeah|ok|okay|sure)[?!.,]*$

2. Strip harness rows from exports

# Python/pandas, applied to a GSC query export
import re

HARNESS = re.compile(r"(?i)^context:\s*location:")
PROBES  = re.compile(r"(?i)(\.\s*my location is usa\.?$|^evaluate the .+ on )")
REPLIES = re.compile(r"^(yes|yeah|ok|okay|sure)[?!.,]*$")

df["machine"] = df.query.map(
    lambda q: bool(HARNESS.match(q) or PROBES.search(q) or REPLIES.match(q))
)
human   = df[~df.machine]          # CTR and rankings computed here
machine = df[df.machine]           # reported as its own line, never deleted silently

3. Same rule in BigQuery

SELECT *
FROM `project.searchconsole_export`
WHERE NOT REGEXP_CONTAINS(query, r'(?i)^context:\s*location:')

4. The reporting workflow

The answer to the Reddit question

The original thread asked whether this is Google testing location-aware agents at scale. The honest answer, from what is public: the string proves location context is being injected into grounded searches right now, for real users, in at least the US and Japanese markets, with explicit instructions to keep that location handling invisible in the answers. Whether that is a controlled test or shipped plumbing is not knowable from outside, and Google had not commented as of this publication. The “(not for language)” parenthetical reads like production hardening rather than an experiment, but the leak itself shows the pipeline is not fully settled: finished products do not drop their scaffolding into customer-facing reports.

Two practical follow-ups if this is in your account. Salvage the question tails before you filter, because they are the only transcript you will ever see of what people ask an AI when your page is in the answer. And watch for variants: the template already varies by country, and the same pipeline can log other harness formats. When a query in your report reads like instructions rather than a question, it is instructions. File it as a machine, keep the question, and keep your CTR honest.

Sources: the r/bigseo thread and its r/SEO mirror (Sept 1, 2026); Suganthan Mohanadasan’s classifier study (Aug 13, 2026); Kimagure Weblog’s independent observation of the Japanese variant; Call Force Global’s 30-day machine-traffic study; Google’s Generative AI reports announcement and report documentation; Search Engine Roundtable and Search Engine Journal on the August 31 worldwide rollout; SEO Sherpa on Mueller’s impression-counting explanation. Inferences about the wrapper’s origin are labeled as such in the text.