HR AI Index
Index Vendors › Agentnoon · September 2026 Edition
2 categories · Named, not ranked

Agentnoon

13Judge labels
3First choices
1Negative labels
7 of 12Models named it
2Categories
September 2026 Edition. Every number here is derived from the raw labels under vendor table vv2026-09.1, every buyer segment counted.
Standing
4 labels, too few to rank
A product needs 10 labels in a category before a share or quadrant is stated. Agentnoon was named 4 times in Workforce planning and 1 other category, where ChartHop led with 31%. The labels and the evidence are below, counted exactly.
By buyer segmentRead the same way at every buyer size.
In workforce planning · each standing computed within its segment · bars are 0 to 100 · the accent bar is the product's own best reading

Standing by category

Every category where a model named Agentnoon for a mid-market B2B company. Share is first choices across the direct, paraphrase, budget and scale prompts; rank is within every product named in that category.
CategoryFunctionShareRankNegative rateLabelsQuadrant
Workforce planningWorkforce planning and analytics2%13 of 890%3under 10 labels · led by ChartHop at 31%
People analyticsWorkforce planning and analytics0%58 of 990%1under 10 labels · led by HiBob at 15%

Movement

This is the first edition on this tier, so no move can be computed for Agentnoon yet. From the next edition this section shows, per buyer segment, whether its share moved by more than the measured noise floor.

By model

How each model treated Agentnoon across every prompt where it was named for a mid-market B2B company. Twelve models, six prompts per category.
ModelFirst choiceAlternativeMentionNegativeLabels
Claude Haiku 4.500101
GPT-5.4 mini00000
Gemini 3.5 Flash00000
Perplexity Sonar00000
Grok 4.1 Fast00000
Mistral Small00000
DeepSeek V4 Flash01001
Llama 4 Maverick00000
Qwen 3.7 Flash00000
Kimi K210001
GLM 4.7 FlashX01001
MiniMax M2.500000

By framing

Which of the six questions produced the naming. By model says how often; this says asked what. The first-choice count on the right carries the marks of the models that produced it.
FramingLabels by classFirst choices
Direct1 labelNone
Paraphrase4 labels1
Comparative2 labelsNone
Budget-constrained2 labelsNone
Scale-constrained4 labels2
Negative0 labelsNone
First choiceAlternativeMentionNegative13 labels in all, every segment counted; 3 of the 3 first choices count toward share, since the comparative and negative framings do not. The bar is one segment per label class, to scale within the framing.

What the models said for it

Verbatim evidence the judge attached to positive labels.

“platforms like ChartHop, Agentnoon, or a well-configured FP&A tool before needing a full enterprise suite” Kimi K2 · Workforce planning · scale prompt · first choice
“Org-level headcount planning and scenario modeling across multiple HRIS.” DeepSeek V4 Flash · Workforce planning · comparative prompt · alternative
“Budget-conscious with per-record pricing | Agentnoon ($4/record)” GLM 4.7 FlashX · Workforce planning · budget prompt · alternative

And against it

Verbatim evidence attached to negative labels. A warning on a product with few labels is a warning; on a product with many, it is one voice among them.

No model argued against it.

Named alongside

The products named in the same answers as Agentnoon, over the 13 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Agentnoon was named but was not.
ProductSame answerTook the first choice insteadHead to head
ChartHop10 of 132Not in the top three
Rippling7 of 130Not in the top three
Workday Adaptive Planning6 of 132Not in the top three
Anaplan5 of 131Not in the top three
Homebase3 of 131Not in the top three
Runn3 of 131Not in the top three
Visier3 of 131Not in the top three
Float3 of 130Not in the top three
Orgvue3 of 130Not in the top three
Pigment3 of 130Not in the top three
A head-to-head page exists where both products are in a category's top three. The other rows are the same fact without a page behind them, so they link to the product instead.

What carried it into the answer

The sites and pages cited by the answers that named Agentnoon. A fact about retrieval, not a lever on the model.

Citations exist only for the models that return a source list, four of the twelve in this edition, so these counts come from 10 of the 13 answers that named Agentnoon and are not a share of its labels.

Domains cited

selectsoftwarereviews.com8
coefficient.io6
peoplemanagingpeople.com6
guideflow.com5
resources.rework.com4
articsledge.com3
charthop.com3
leapsome.com3
learn.g2.com3
lupahire.com3

Forty-four of the forty-four domain citations in answers naming Agentnoon came from somebody else's page.

Pages cited

Pages are listed as the models cited them.

Is this your product?

Claim this page

Claiming is free and changes nothing in the data. A claimed page shows a verified contact who is told when each edition publishes and when Agentnoon's standing changes by more than the noise floor; the right to propose corrections to the vendor table, meaning names the judge wrote that should or should not read as Agentnoon, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.

It does not get any change to labels, shares or verdicts, any preview, or any say over which quotes appear. A verification link goes to your work email; an address at agentnoon.com is approved on the spot, any other address is reviewed by hand.

Your name and company appear on the claimed page, or the company alone if you ask below. A title and a LinkedIn address appear there too if you give them, and are left off if you do not. Your email address is never published.

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