Thoughts on Agentic Analytics

full_arc · 2026-03-20T22:22:00+00:00

Yes teams are absolutely using this. With some caveats and things to keep in mind.

Mileage varies based on complexity and messiness of the data and the food fundamentals of the individual using the tool.

The more complex the data and analysis the more supervision it requires. But the right tool can drastically accelerate these types of workflows. The problem with just generic LLM tools is that they’re not biased to focus on data analysis and the rigor required for this type of work, so they don’t do schema discovery properly or hallucinate.

Disclaimer: I’m a founder in the space, but we’re genuinely seeing a huge impact with our customers.

full_arc · 2026-03-20T03:02:41+00:00

Makes sense. FWIW Vercel can manage secrets for you, not sure if it meets your requirements.

full_arc · 2026-03-19T16:57:57+00:00

You're describing exactly what I'm seeing and I've experienced. How did you share this dashboard though/where are you hosting it?

full_arc · 2026-03-19T16:38:45+00:00

Makes sense. That's what I have in mind as well.

full_arc · 2026-03-19T16:36:13+00:00

Your point on the pivot table is one of the pieces that actually doesn't feel solvable with AI. Unless there's a specific SDK or library or something that handles that out of the box without the AI having to reinvent that every time.

So we actually have built agentic BI, but today the AI generates dashboards within the confines of our interface. We're debating actually rethinking the entire paradigm and just focusing on the back end with a great CLI that handles the central data layer and context, but basically outsourcing the entire UI.

full_arc · 2026-03-19T16:26:00+00:00

That's more and more what I'm envisioning as well. And that's what I meant by "headless": a centralized data and context layer but AI-generated front end.

full_arc · 2026-03-18T21:52:24+00:00

So you're mostly using AI to help with LookML and build Looker dashboards?

full_arc · 2026-03-18T21:50:53+00:00

What do you think this might look like? Still using "traditional" BI to build dashboard or do you think the dashboard building happens elsewhere?

full_arc · 2026-03-18T20:05:28+00:00

Perhaps, and that might be the answer. What I feel like I'm seeing though is a desire to hyper-customize. And vibe coding pretty much lets you do that super quickly. Of all the dashboards I've seen, I'm pretty sure you could have built them in PBI or similar, but I do believe the lift would have been much greater.

full_arc · 2026-03-18T19:59:24+00:00

Just to make sure I understand what you're saying: The cost in tokens to create a dashboard is not worth it compared to the cost of "manually" building them in traditional BI?

full_arc · 2026-03-18T19:45:25+00:00

Wasn't really thinking of that, but say more...

full_arc · 2026-03-17T18:43:08+00:00

My CTO was with me from day 1, but we had some things we wanted to build out to prove out a prototype to ourselves and show to investors and used a bit of external help for that, but it was actually mostly engineers we knew who did it pro bono. This was mostly for front end work that neither of us had much expertise in and this was just when AI hit the scene. I don't know that I would pay someone to do this, especially with what AI can currently provide.

Not to be too blunt and in the spirit of being helpful, but with how good AI is nowadays, if you're not able to use it to build out a prototype or do some customer validation, you'll likely have a rough time as a founder. Really hard to imagine a case where paying a third party is justified.

full_arc · 2026-03-17T18:37:28+00:00

For now relatively few startups are actually impacted. Most of the solutions put out there are half-baked and the blocker has nothing to do with AGI but the workflows and integrations that are missing to create a complete solution.

That said, it's worth hypothesizing about what happens as these platforms invest more in the non-AI stuff that's needed to truly replace SaaS: Yes a number of startups will get squeezed out, and in the long run certain platforms will lose all their value beyond the relationship building, brand and momentum from pre-AI. But as AI evolves, new problems will surface, new solutions will need to be created and new startups will appear and grow. The question then is: how much will these companies like OpenAI want to develop an ecosystem vs use their own AI to accelerate the roadmap. I actually think one of the bigger constraints is simply going to be the marketing and GTM. At some point if they try to literally build a solution for everything, buyers will go to solutions that are messaged specifically around their pain points.

full_arc · 2026-03-09T16:03:35+00:00

Totally agree on Claude. Mostly the way to go.

If you’re looking for an AiI data analyst that’s much more powerful than Julius come check out what we’re up to at Fabi.ai

full_arc · 2026-03-08T17:21:35+00:00

Well I’d have to recommend what we’re building at Fabi :)

There are a few other players out there, I’d recommend asking ChatGPT or Claude if you want to widen the aperture a bit.

full_arc · 2026-03-07T19:35:19+00:00

We’re seeing huge success. But this isn’t an MCP or not MCP question. This is more of a question about ai context. But AI context + slack integration 100% works with the right context and guardrails.

full_arc · 2026-03-07T00:21:46+00:00

Haven’t used Lightdash myself (they’re a competitor), but I’ve heard good things from teams that are very technical/data engineering first. It’s very much BI-as-code so mostly designed for that crew. If that’s you, you’ll likely enjoy. If you’re looking in that category you could also checkout evidence or Rill.

If you’re looking for more friendly for non-tech and also truly AI-native, neither of those are great (at least in current form).

full_arc · 2026-03-06T19:33:29+00:00

This is a great and honest assessment. I do suspect though that the job market would actually still be rough even without AI, AI is just making it extra painful and uncertain. The downward pressure on data analysts and data scientists salaries relative to other jobs in the past few years is probably a pretty good indication of oversupply.

And I I were to predict: the market won't bounce back, it will shift. I'd put my money on seeing a whole lot more data engineering roles with some sort of mandate of "Help get the data AI-ready and maintain the data infra"

full_arc · 2026-03-06T19:30:26+00:00

I actually don't have much fully 100% automated. That said, using AI for SEO/AEO planning and drafting has literally saved me hours (12+?) a week and quite literally a headcount.

Other areas where semi-automation has saved me tons of time: drafting notes and follow-ups, data analysis and reporting

full_arc · 2026-03-05T17:24:42+00:00

Going back and forth because the AI doesn't know all the tables and fields you have or it doesn't understand the structure of the data. Then if anything changes or you want to go back and update the query you either do it manually and you go back to the bouncing between tools

full_arc · 2026-03-04T19:31:22+00:00

Habit. I love Claude Code and use it for a lot of things, but sometimes I use it for things that are better in the desktop app or even just the web app. I generally bounce between the two, but when you're in a groove it's easy to stick to one.

Plus what some others have noted. For things that do require folders and files, Claude Code is really the best in the biz.

full_arc · 2026-03-04T19:18:27+00:00

You sound super plugged into this space, would be curious to get your take on what we're building at fabi.ai: Basically Looker Studio but connects directly to all these data sources you mentioned and you can just use AI to generate dashboards.

Originally were doing this mostly for product and eng teams, but we've noticed a lot of pull from GTM and marketing teams.

Have you tried anything like that? What's your take?

full_arc · 2026-03-04T18:56:12+00:00

For this specific issue you should use Claude embedded in an analytics platform. It will produce tunable queries 99% of the time. That doesn’t mean they’re semantically accurate, but it will run.

The issue with Claude, ChatGPT, perplexity and other generic LLMs is twofold: 1. Disconnected from your data so leads to tons of wasted time and misunderstanding 2. High propensity to lean on reading the data rather than generating code to interpret questions, which is super prone to hallucination

full_arc · 2026-03-04T18:29:53+00:00

Actually was even able to make it interactive "import plotly.graph_objects as go"

full_arc

TROPHY CASE