The sentence is near incomprehensible without knowledge of the Claudish robolect. (Here I made up a word. Hopefully its comprehensibility isn't gated too much on language nerdery.)

"reliability gates agentic analytics" is quite a garden path sentence, at least to the eyes of a non-native speaker.

I read this whole readme and I have no freaking clue what this does beyond "searchable knowledgebase". How about providing some use cases?

Thank you! After reading the very first line I just had to check the HN comments:

> Agents can retrieve documents and query databases, but reliability gates agentic analytics: a revenue metric answered faster is worth nothing if the number is wrong.

Absolutely no human would ever write this kind of slop. I have no idea what this even means. Is this really the first thing you want users to read?

I wish people would take a fucking minute to at least figure out what their main selling point is and just write it, you know, like we used to.

> Agents can retrieve documents and query databases, but reliability gates agentic analytics

this reads like an attempt at poetry

i had exactly the same issue. That first sentence triggered the bad experience i had with claude opus 5.0 interactions and i just closed the page immediately.. All of the PRs that I have seen with "gates"... it really impacts me

yes pls linguists track these word overuses and help us get rid. Everytime someone says Gate IRL i lose my train of thaught

with this i agree whole heartedly, i can tolerate clanker code in exchange of the amount of value it can generate after heavy polishing, but good writing is something AI is leagues behind, so far back that is even making human writing worse by imitation. Use it for grammar, use it for punctuation, never let the clankers write by themselves and call it a day

And yet it still contains what looks like human error, e.g. the "Software Components" table mixed up the columns for the database.

Is that first diagram also pure slop? I would have to understand more of what they're doing to confidently say it is, but it seems odd.

All the four nodes on the right side have either only incoming or only outgoing arrows.

Especially with the resources of Nvidia FFS

Seriously? This is a professional assessment?

> If a tool is worth building and sharing, surely it’s also worth writing a brief summary of what it’s supposed to achieve in your own words?

Next the complaint will be any such description looks like AI slop.

what happens when your column names dont accurately represent what data is being stored (i know i wish it did but it doesnt at my place right now) and its all esoteric knowledge held by our data scientists? my worry is now the guys above will think its answering their question without knowing that its either A. wrong or B. data they may not be able to use for legal reasons on the project they are trying to make.

Discovering ontology in complex real world large systems (involving multiple microservices) is quite hard. Best way IMO is to learn the database schema details, cross table joins etc from source code itself as it also has business context. This has worked for me quite well. But there are many other challenges like KPIs have fixed queries, cross product queries, pulling large data and getting right architecture for joining in-memory.

"seems a lot of folks here didn't grok the very terribly written README"

I tried to make it through the README and it was just unmotivated slop. Perhaps it reads better to agents. It is like the Van Halen rider story - if the authors didn't care to write a good README, what are the odds that they paid attention to the rest of the large codebase?

> Fixing unreliable agents by adding more unreliable agents.

Depending on how you combine imperfect systems, you can lower or increase error rates.

> data they have learned about the world, instructions and data under analysis.

I don't think there really is a clear distinction between these things though.

And I don't mean this in a gotcha way - these things apply to people too. Not that that means they're like people, but we have successfully built enormous systems out of fallible people, and other imperfect things.

The goal is rarely "eliminate all errors" but "have a thing that's useful, given a possibility of things going wrong".

Yes, also I hate the direction "frontier" is taking now. Opus 5.5 may be better in agentic coding and reverse engineering, but it has regressed greatly in instruction following.

You can give it instructions and 5k tokens later it will do exact opposite you told it to.

> Fixing unreliable agents by adding more unreliable agents.

I think the 'reliability add' is from how it's presented back to the user. If you can trust the agent to at least say what tool it called, then you can mark a chart as being the canonical output of "get_projected_profit" tool vs "get_realised_profit" tool and everyone knows it hasn't come down to the current dice roll of randomness what the agent itself did this time.

> You can ask them only to consider the 5 facts in front of them, but they won't do that, and cannot do that.

Has training them to do it been tried and proven to fail?

Reliable TCP was built on top of unreliable IP. Our body chemistry is another example of unreliable non-deterministic processes creating more deterministic outcomes. Yes, yes, it's all different. The common idea is the same: we can get reliable results out of unreliable systems.

Since agents are more often right than they are wrong, combining them can increase reliability.

They are attempting to fix an inherent problem with the technology via epicycles.

Funnily enough, I commented earlier in the thread, but this benchmark is a collaboration between $DAYJOB and the university =)

Is this similar to Datalog where you have facts and rules?

why would i use this over some rdf / owl / sparql thing?

i want to use these semantic / KG tech but i find them so confusing!

The amount of finite rules necessary (that the LLM will write) to handle the noise and complexity of real data generates such a dense graph that it breaks the premise of a human-maintainable and auditable system.

sorry this may be way off, but feels analogous to me.

reminds me of http://www.thai-language.com/ (it's safe). it was around long before Google Translate got good enough. i don't know the authors/maintainers, but afaik, everything is entered manually. this is the "analytical" approach to language. most translations before Google Translate went full ML used an analytical approach.

anyway, long story short, turns out "analyzing" a language to "understand" it GREATLY underperforms probabilistic/statistical methods like ML.

also reminds me of trying to label parts of speech in Vietnamese. there's a school of thought that finds it useful...then there's another school of thought that plainly points out words that can be verbs, nouns, adjectives all in different contexts.

We can and do! Here's one you can help maintain and review!

https://www.wikidata.org/wiki/Wikidata:Main_Page

Define reviewable and maintainable

Maturity of the storage and query engines:

SQL > Cypher > SPARQL

You can do RDF/SHACL/OWL with LPG/Cypher based databases.

Ontolite (sqlite) and ontobug (ladybug) are examples.

Few thoughts:

- you need some engine to support your owl/rdf/sparql, and all available are lower quality/perf/features than pg

- its easier for LLMs to talk standard SQL compared so some version of sparql

I am new to this field and our org struggle with authoring and documenting existing implicitly given ontologies. It is like working with archeology, and proposed automated solutions like nvidia’s seem alluring in that regard. What would be your suggestion for a way to extract the ontology/RDF/OWL/graph from existing underdocumented fields?

Aren’t these data formats and query engines? You still need something to extract and maintain the ontology no?

Shameless plug: we built our own open source equivalent of auto-ontology a few months ago, that doesn't require any API key but your LLM one (or none if you use your coding agent subscription)

https://github.com/GetCassis/ontology-skills

I've not tried running it but while it defaults to NVIDIA hosted endpoints but the readme suggests you can host those yourself if you want.

You can self-host the LLMs involved, API key is only if you're using nvidia-hosted

Ontology is a so-called white-box approach for AI: all dependencies are clearly stated and formalised. If something does not work as you wish, unlike LLMs, in ontology world you can clearly diagnose where the system fails. I understood the NVIDIA's idea this way: we take unreliable LLM and make a "reliable" ontology using it. If you look back into history, Prolog , was exactly a sort of "memory system" or, better put, a human-friendly database. Ontology is a further development of this concept.

This is more about streamlining the Date Engineer / Data Scientist role. Business Intelligence, in other words.

Before: domain expert wants to ask a question about the data, but which of these hundred tables actually has the right “revenue per User” column? Ask a DE to help slice the data and bless the queries.

After: ask your agent. The agent looks at the ontology and can usually find the best blessed data. To enable this you need to write down all the tribal knowledge that lived in the DE/DS head; agents can take the first pass at proposing and updating metadata.

Under the covers the DE and DS roles switch to reviewing the underlying ontology, instead of working directly with business stakeholders on one-off queries.

One issue in a business is what does all the data actually mean? What are the various definitions and how do they relate to each other. What's a customer? Should that include people that have bought things, or anyone who is potentially buying? Do different systems use the same definitions, what are the columns, etc.

An ontology in this kind of context means a more precise machine readable spec of this and how it all relates to each other. You can totally build one by hand. The idea here is I think that it tries to figure it out itself and can take corrections over time, so you can ask natural language questions and get more reliable results rather than hoping an llm works out what "customers this month" means the same way each time over a wide span of data rather than having a much clearer API to work against.

A memory system is managed by the RL trained behavior, these systems use specific prompts and ordinary software to hardcode a policy. The RL version is usually more efficient, but it can't be improved or made more repeatable.

I happen to program management at scale and this is indeed very relevant to my interests.

Imagine if you will, 10 groups inside a broader org. Each of those groups have teams that work on different stuff, but at the org level and cross team level managers and leadership still need to make sense of it all and if you ever want to connect the dots across work you need a common language.

Now multiply that problem across a large enterprise if you want a company brain or any of the context to be trustworthy for usage at scale or for high accuracy and trust work.

This is a a tooling solution for the schema and government layer that enables that so you can have an empowered human say "what team X calls Foo, and what team Y call Bar should map to our approved definition for Baz and here is how we validate their data against that shared classification."

they refer to Ontology as on Business Language for an enterprise.

This isn't really anything to do with ontology in the philosophical or academic sense, outside of computer science.

In software, an ontology is just a machine-readable definition of the entities in a system - their names, properties, relationships etc. This tool analyzes a system and produces such a definition for agents to use.

just ask your agent to install it, i think you will need an NVIDIA key, but i think you can bring your own model too.

Enterprise like technical debt