I am interested as well what future would look like. So far what I am seeing is:

(1) specialized AI agent -> (2) we should add 1790 agents to be competitive -> (3) pivot to agentic workforce platform

now we have lots and lots of agentic workforce platforms and sandbox providers to run them. All have similar capabilities: create agent for HR, create agent for Sales,...

Hope to see something interesting to pop-up, at least it was happening in SaaS-era where people were inventing new ways of solving old problems: DocuSign, Salesforce, Zoho,...

The thing that AI is best at is summarizing vast quantities of information. That means the most natural thing for an AI to do is be "the one tool to rule them all".

The more information it has access to, the more useful the answer can be. But that also means that it can answer all the questions.

I'm tired of endless LLM spam submissions from people who only use their accounts here to advertise and self promote.

Developing and serving GenAI models is highly unprofitable, so, no, we're not going to have that in the AI world.

Either those model developers & providers package them in as many services as possible so that they can be somewhat profitable, or they die, and we don't have model developers & providers anymore.

So all your competitors can do exactly as good of a job as you. What's to differentiate your offerings from literally anything?

Do you think customers are _eager_ to do business with a CEO less entity?

The article mentions that there’s an identification process and that at least some data has access control. What were you expecting?

I see two vectors here

- The bot giving out PII by accident. You ignore it and report it.

- You trying to fool the bot into giving you PII you're not supposed to have. But you've created an audit trail of your 100 failed prompt injections. The company fires you.

This isn't public facing, open to anyone. This is more like a shared printer in the office.

In the strongest interpretation of that it would offer only data which the user is allowed to access. Why do you assume that them implementing a feature to prevent PII being accessed that they then turn around and return data which the user is not supposed to access?

Is the result good? Is it useful?

And why does your comment say you're a 30-person company but the title says 60?

> The bot took two and a half weeks to build; the data infrastructure under it took two years.

This is the key lesson that everyone needs to step back and pay attention to here. The data is still king. If you have a clean relational database that contains all of your enterprise's information, pointing a modern LLM (i.e., late 2025+) at it without any further guidance often yields very good outcomes. Outcomes that genuinely shocked me no fewer than 6 months ago.

I am finding that 100 tables exposed as 1 tool performs significantly better than 100 tables exposed as 10~100 tools. Any time you find yourself tempted to patch things with more system prompt tokens or additional tools, you should push yourself to solve things in the other ways. More targeted & detailed error feedback from existing tools often goes a lot further than additional lines of aggressively worded prose.

I think one big fat SQL database is probably getting close to the best possible way to organize everything for an agent to consume. I am not going to die on any specific vendor's hill, but SQL in general is such a competent solution to the problem of incrementally revealing the domain knowledge to the agent. You can even incrementalize the schema description process itself by way of the system tables. Intentionally not providing a schema description tool/document/prompt seems to perform better with the latest models than the other way around.

I just did the exact same thing for my company. I didn’t do the sql lite approach for gdrive though just a direct search.

The one part that is still difficult is the data modeling and table level descriptions etc. Maybe you make an update to a table - remove a column, etc. The 3rd party systems all have their schemas defined but the data warehouse is a bit more loose. So solving that really helps. Did you just use dbt schema to describe tables and columns then sync that to your bot? How did you keep it updated? And end of the day - worth building or buying? Also how did you track costs? I let users choose their model - but have learned it can get expensive fast. As I can see there are a lot of providers trying to solve this one thing. That said the data warehouse aspect is the loosely defined area and I can see dbt or one of those players try to build something.

Hi. thanks for sharing. One thing I'd like to know is how often do you validate the answers? If a human gives an answer like the one the AI is giving for example, you'd probably expect a margin of error of like 1% of making a mistake. The AI though, is it 1% or less - and who's validating it? Are you trusting it more or less than a human?

Nice to see you getting out now and then Rorschach.

If I order now, do I get a second set for free?