In this case (https://agents.withgauge.com/p/runs/40be763c-4678-4677-88df-...) the agent wrote a python script to test the site, and hit the error above. We don't have a full filesystem diff viewer in ax-check.com, although we do have that in the Gauge agents product itself.
Interesting, thank you. I'll check whether a setting on CF might be interfering. The site checks headers for routing and response types, but it shouldn't affect access.
which URL is it? it's kind of a deep assumption since we're evaluating on ease-of-navigation from the homepage, and looking for /llms.txt etc. but there should be a way for me to add support
Each product is different (how much does it cost to serve a marginal user, how risky is it). But generally I think allowing ephemeral accounts like Cloudflare, or serving simulated/static accounts before a human claim can both work.
For a lot of products it can just be an extension of the free tier I think too.
Agents are the new search engine - a majority of people are starting to get recommendations and answers from them. So asking this is like asking "what is the benefit of making any resource more available to search engines?"
The answer is discoverability. which is pretty critical for any business
I've heard CTOs saying they don't consider buying any product that is not AI agent friendly. Which means they must come with AI skills, MCP Servers and so on that agents can use it to do stuff. You may not like it, but that's almost certainly going to be the future.
Behind an agent is a human that might use the product. I think customers/consumers will demand that their personal/subscription agent can access the product.
As someone that works for a company that gets a 100% score on ax-check, all the effort I've put into making it accessible for agents has not 10xed the growth numbers like I was told it would.
The Ai growth looks more like the very-long-term 2% (technically productivity growth from innovation), avoid assuming the hypesters are even close to accurate in their predictions
The ROI is for we the users, not the companies, only issues is that the agent's attention is way easier to hijack, so you have to maintain a different kind of attention when using them and never blindly trust their outputs.
agents are becoming the de facto standard interface for everything, whether you like it or not, so it depends on the intent you have for a project, or "product" as OP phrased it, which has a more contextual implication that the intent is to get others to buy it
ironically, I did sell a kirby many many years ago
the idea is likely way more about business purchases and SaaS than consumer products, though I did have an agent do deep research to give me options when deciding on a new mattress
The initial step of the scan looks at homepage content to try to figure out where to navigate next, it's getting confused by all the different products linked. Will fix this!
Gauge | Founding Engineer | San Francisco, CA | ONSITE | Full-time
Gauge helps companies show up in more AI answers. Search traffic is shifting away from Google into AI chat, and companies are figuring out how to adapt right now. Our system delivers real-time analytics derived from millions of LLM answers, while providing an intuitive, powerful UX. We also generate optimized articles to improve our customers’ visibility, and offer an agentic chat interface over their data (think ‘Why was there a spike on 12/10?’ or ‘Which of my pages should I refresh?’).
I'm Evan, the CTO, and we're looking for engineers with strong programming fundamentals, creative problem solving skills, and a high-agency working style. You might be a good fit if you are an ex-founder, ex-founding engineer, or have worked at a Seed/Series A startup.
We primarily use Django, React, Postgres, Celery, and Clickhouse.
Contact me with your resume (and mention HN) if you're interested: evan[at]withgauge.com
Yup I definitely see confusion in our responses around the product and brand names. We do another pass through an LLM specifically aimed at ‘canonicalizing’ the names, but we’ll need to get more sophisticated to catch most issues.
In that case you mentioned, the brand confusion is what accounts for the top three omission for QBO. Both OpenAI and Perplexity rank it #1, but Anthropic ranks the slightly different “Quickbooks” product as #1. Our overall ranking prioritizes products that appear in all three responses, so both are dropped down.
Interesting, I thought it might be something like that.
Yea, 'canonicalizing' is really tough (although I don't know if you really need to get it *perfect*) because what is correct is different in different contexts.
Accounting Software as an example again, for the category overall canonicalizing any reference to Quickbooks to the same company makes sense. If you're asking about more specific recommendations though 'Accounting software for sole traders', you might have both Quickbooks Online and Quickbooks EasyStart mentioned, and they are actually slightly different products. Or Netsuite is actually a suite of products that might all make sense in slightly different contexts.
I get the output from the LLMs, compile into a report, and then pass it back through an LLM to sense check the result with the added context of what's been requested in the report, but I'm not super happy with the outcome still, some different categories still come out a bit of a mess.
It's from previous searches actually, we have an 'enrichment' step after the initial rankings come back which helps with semantic deduplication and tries to give us a canonical website domain. We store the Product and tag all matching rankings: https://productrank.ai/product/microsoft and use a 3rd party to map website <-> brand logo.
```
>>> request.urlopen("https://anc.dev")
[...traceback info...]
urllib.error.HTTPError: HTTP Error 403: Forbidden
```
In this case (https://agents.withgauge.com/p/runs/40be763c-4678-4677-88df-...) the agent wrote a python script to test the site, and hit the error above. We don't have a full filesystem diff viewer in ax-check.com, although we do have that in the Gauge agents product itself.
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