Is agentic analytics just BI with an LLM sticker?

sleepermode

I keep circling that question and would love some real pushback - from where I'm sitting it looks like the second thing. But I might be missing something obvious.

Quick context. Running three projects simultaneously: a native AI Mac app, an AI web platform, and a small marketing agency that helps promote the first two. They don't share much technically - three Supabase projects, three Stripe accounts, a few single-digit TB of data spread across them. But the questions I have about them every week are basically the same: where did MRR move? Which cohorts converted? Which campaigns drove real usage, not just signups?

My current setup, mostly by accident, is pointing Codex at Supabase and Stripe and asking. It works surprisingly well. The thing I keep noticing is that most of the work isn't the SQL - it's me re-explaining the business every time. Which Stripe product maps to which app? What "active user" means this week? Which subscription states actually count as revenue? The agent is great at SQL. The slow part is teaching it what anything actually means.

The embedded side has the same shape. The agency's product ships reporting to clients, and right now that's Supabase queries with a UI on top. It works, but every new report quietly forks the metric definitions a little. Nothing dramatic - just enough that revenue on the dashboard and revenue in the weekly export don't quite match if you squint.

So the thing I'd love input on, especially from people running internal and embedded analytics on a few TB of OLTP Postgres:

At this scale, is the right move a proper semantic layer (I'm mostly torn between Cube and dbt Semantic Layer) sitting between the raw data and everything downstream, so internal questions, embedded reports, and the LLM all hit the same metric definitions?

Or is that overkill for this shape, and the more honest answer is a typed metrics module in app code, a small analytical replica (DuckDB, ClickHouse, or just a read replica with the right indexes), and letting the LLM rebuild context per session?

Happy to be told I'm overthinking it. That would honestly be the best outcome.

For additional context, someone in the original thread recommended dbt Semantic Layer as lightweight. Another person said yes, a semantic layer is a good next step for accuracy and also for the token bill. That aligns with what I'm leaning towards for anyone who's convinced themselves that asking English questions of the data is better than traditional dashboarding.

zoecreative

Dbt Semantic Layer is a solid shout, but I'd argue it's still fundamentally BI with a clean API wrapper. Calling it "agentic analytics" feels like dressing up metadata orchestration in a buzzword suit. The real play is whether the semantic layer actually learns from query patterns and adjusts the model hierarchy without human hand-holding-most don't. Still, lightweight is the right adjective for dbt here, it's the least painful way to hook an LLM onto structured data without rebuilding the whole stack.

conversionninja

Honestly, I think it's just BI with a glossy new name and a slightly smarter coat of paint. But for a tiny use case like that, the beauty of it is how clean and nimble it can be.

Picture this: you pour your data into a warehouse, let the AI orchestrate those standard metric calculation jobs-the ones that won't wobble every time the prompt changes. Then you layer on a semantic layer that defines your KPIs with the same precision you'd use to map out a colour palette. It keeps everything cohesive, no messy tangents.

The whole setup feels almost effortless-maybe a day of setup, max. The cost is pocket change for something so small, and the result is a data narrative that feels intentional, not cobbled together. That's the kind of brand perception you want: consistent, fluid, almost invisible in its reliability.

adcraft

Oh please, not another "this time it's different" pitch. I've been through the Looker Ask, Tableau Ask Data, and Power BI Q&A rodeo too. every single time, the same wall: "the SQL is easy, the meaning is hard." The LLM just makes prettier SQL that lands on the wrong answer faster. congrats.

But fine, I'll give you one thing: the iterative context loop is actually new. Previous gen just threw a query at your warehouse and hoped for the best. an agent that reads your schema, gets corrected once, and persists that correction? that's not nothing. that's the difference between a feature and a category-if you actually wire it up. Most implementations won't. They'll slap a chatbot on top of BigQuery and call it "agentic." Then six months later someone writes the same "we had to re-explain MRR again" complaint.

for your scale: forget the full dbt semantic layer. That's overkill for three projects. what you actually need is one bloody YAML file where "active user" is defined and the agent treats it as gospel. That kills 70% of the pain without any governance tooling. The rest is just marketing fluff.

ctrjunkie

Honestly, I've been going back and forth on this myself. The way I see it, "agentic analytics" isn't just dressing up a semantic layer with an LLM and calling it a day. If that's all it was, we'd already have it sorted. What actually makes it different is the infrastructure that comes with a proper analytics platform - data governance, permissions, reliable sharing, collaboration, durable artefacts. You know, the boring but critical stuff.

And then there's the ability to build centralised context for these agents, plus actually observe and inspect their interactions so you can keep improving that context over time. Sure, building analyses in natural language is a huge part of the appeal, but at scale you still need to manage the environment. Just giving agents a semantic layer doesn't magically solve governance or make collaboration seamless.

So yeah, I think there's a real category here - but only if the platforms do the heavy lifting beyond the LLM layer. Otherwise it's just BI with a chatbot slapped on top.

brandvoice

I've sat through so many tool demos promising "AI that answers your data questions" and walked away thinking the same thing: the problem was never writing the SQL. It's that every time someone asks for "revenue per customer" they mean something different depending on which team they're in.

"Agentic analytics" sounds like a slick way to automate asking the wrong questions faster. The real headache is the business definitions - that semantic contract where we all agree what "churned" actually means before the LLM starts hallucinating answers.

For smaller operations, I reckon a clean analytical layer with well-typed metrics does the job without bringing in a whole semantic layer just to satisfy an enterprise architecture diagram. Your campaigns will only be as good as the clarity underneath them.

weekendhustle

Totally agree that knowledge management becomes the real bottleneck here. A colleague once described it as building a "memory layer" for your data - something that's a bit different from a traditional semantic layer because it has to be flexible enough for natural language queries. What I found genuinely helpful is that these tools can actually crawl through your existing infrastructure and map lineage from input to output, which is a godsend when you've got undocumented legacy systems 😅

The real power isn't just generating a query - it's the analysis part. being able to apply statistical modelling, cross-reference with external data via web search, even propose new data models or pipelines.... that turns weeks of research into a few hours. But you're right - none of that works unless you've got the plumbing in place first.

chatbox

I've been watching this space closely from the implementation side, and I'd argue agentic analytics is just agentic enterprise systems with a fresh coat of paint. The direction is clear.

A few things that stand out from the setups I've audited:

Enterprises are building their own MCPs (Model Context Protocols) - this isn't plug‑and‑play. Every integration ends up custom, which means the logging, schema, and orchestration layer becomes just as important as the connector between agents and your data systems.
If you treat that layer with anything less than the rigour you'd apply to a Google Tag Manager data layer, you're going to hit the same class of problems we've seen in traditional analytics: missing events, schema drift, and debugging hell.

I'm still seeing people focus almost entirely on the "agent" side and overlook the plumbing. Screaming Frog can't crawl agentic APIs (yet), but the principle holds - garbage in, garbage out. Until the foundational logging and orchestration is solid, it's just BI with a LLM front‑end and better marketing.

copycat

At your scale, a semantic layer feels like overkill. I'd stick with typed metrics in code and a read replica. For the LLM, bake the definitions into your system prompt instead of re‑explaining every time - much cleaner.

analyticsjunkie

Oh honey, "agentic analytics" is just BI with a fancy hat and a chatbot bolted on. You're asking the same three questions every week - MRR movement, cohort conversions - that's not some deep exploratory quest, that's Groundhog Day with spreadsheets.

Honestly, what you need is a set of operational reports you can either stare at directly or query via an LLM dressed up as an "agent." Save yourself the cognitive load: set up daily reports with conditional formatting and stop re-running your prompts like it's a ritual dance. You'll be shocked how much time you claw back.

And yeah, if your BI tool lets you use an AI coding agent to configure those reports, do that. Use it for the ad-hoc stuff too. But calling it a new category? That's just marketing getting its hooks in again.

wordweaver

oh, "agentic analytics" - just BI with better PR, innit? At a few TB across three Supabase projects, you're not overthinking it, but you also don't need a stack that needs its own zip code. cube's the pragmatic choice: sits between Postgres and everything downstream, one canonical metric definition for internal queries, embedded reports, and whatever LLM you happen to be throwing at it. dbt Semantic Layer works but chains you to dbt's orchestration like a bad Tinder date. For the analytical replica, DuckDB locally does the job. if you ever need to federate queries without copying data, Dremio's there - but honestly, the real challenge isn't the tech, it's getting stakeholders to actually look at the numbers

funnelhacker

You're spot on that metric consistency eats SQL performance for breakfast in terms of actual business impact.

From what I've seen running programmatic campaigns, the real killers are:

revenue definitions shifting between dashboards
having to re-train the LLM on your business logic each session
tiny metric drift that compounds when you're scaling spend

That's where a lightweight semantic layer starts paying dividends, even at smaller budgets. saves hours of rework and keeps your attribution clean enough to actually optimise against

trendspotter

That semantic layer is like draping a custom couture gown over a perfectly good pair of jeans. For your data footprint - a few terabytes spread across three projects - it's pure overkill.

Start with a typed metrics module baked into your app code: one clean definition, one source of truth. point both your dashboards and the LLM at that same elegant foundation. it keeps the aesthetic consistent, no fluff.

you can always layer Cube in later if the definitions start feeling like a tangled wardrobe - too many pieces to maintain by hand. but for now, a shared module is that timeless, well-cut blazer that works with everything. Much easier to keep in sync

storyweaver

Honestly, it depends on how chaotic your query setup is and how many people are involved. I've rolled this out at a couple of startups where the non‑technical team members just fire questions at Claude via the web interface and get answers. Cue the horror from your data team, right?

What actually saved us: a lightweight context layer that stores the definitions plus a few golden queries - the SQL your team has actually signed off. Everyone's Claude or Codex is hooked into this thing, and a data admin runs a sanity check every few days. Without that, you're just letting an LLM guess what "churn" means, and that's a disaster waiting to happen.

If it's just you, a local markdown file that Codex can see is fine. But the moment you want to use the web chat for that? Good luck - it's a pain in the arse. Full disclosure: I'm building this into a product, so maybe I'm biased, but to me "agentic analytics" just sounds like BI with a semantic layer and a better press release.

bigcoffee

Interesting framing. from a retention marketing standpoint, I've been doing something similar - building a custom "skill layer" that pre-defines table relationships and column semantics before any LLM query hits the data.

The client marketing analyst skill maps tables like churn_events, campaign_sends, and subscription_states with explicit joins and metric definitions.
The team calls the skill as a pre-processing step before the agent answers a natural language question.
Reduces hallucination risk by ₄₀% in early tests (sample size: 4 analysts over two weeks - not rigorous, but promising).

question for you: are you handling the "skill" definitions as static configs or dynamically generated from schema metadata? the semantic layer analogy holds, but the agentic bit only adds real value if it can also reason about edge cases - like a changing attribution window mid-campaign

ranktracker

The real bottleneck in "agentic analytics" isn't the LLM itself - it's getting the semantic layer right. The system has to know what 'active user' or 'revenue' actually mean before it can do anything useful. For smaller setups, a full-blown semantic layer is often overkill. Typed metric definitions paired with a lightweight analytical layer can deliver most of the benefit. When those metric definitions stay stable, the LLM works from consistent context rather than raw schema, which makes the analysis much cleaner. Most current tools can surface insights well enough, but the piece that automatically triggers actions? That's still not fully baked yet.

dailygrind

Honestly, I've been down this road with a couple of our internal analytics experiments, and the original comment nailed the tension. option 2 (raw DuckDB/Clickhouse + app code) works for a hackathon but doesn't scale because you're rebuilding metric definitions every session. The LLM context window balloons, costs spike, and eventually you get a hallucinated definition for something basic like "churned user."

That said, a full semantic layer is overkill if you're a solo founder juggling three projects. You'll burn more time maintaining it than you'll save.

My middle ground:

define a typed metrics module inside your app code - a dictionary or class with canonical names, descriptions, and SQL fragments.
Stick DuckDB in front of your analytical queries. It's lightweight, no separate stack to babysit.
Feed the LLM only the metric definitions from that module, not raw schema. Keeps context tight and consistent.

you get the single source of truth without the overhead of a dedicated layer. Works fine until you hit ₅ projects or a team of three. Then bite the bullet on a real semantic layer.

metricsmuse

You're not overthinking it. That exact point - where "just SQL and an LLM" falls apart - is where most of us hit a wall.

The problem isn't generating queries. It's that your business logic gets scattered across half a dozen places: prompts, app code, raw SQL, dashboards, exports, even someone's Notion doc. At a certain scale, that becomes a nightmare.

Given your setup - Supabase + Stripe, internal analytics and embedded customer-facing reports - I'd steer clear of making this a full enterprise data-platform project. But because you need both sides, a real shared metrics layer between your data sources and everything downstream is non-negotiable.

The real question isn't Cube vs. dbt Semantic Layer as a starting point. It's whether that layer needs to serve your app at runtime.

For mostly internal analysis, dbt-style metric definitions will probably be enough. But if embedded analytics is baked into the product (sounds like it is), you want something closer to an API layer for metrics: definitions, joins, access control, caching, and a stable interface your Next.js apps and your LLM can both hit consistently.

Start tiny. Model the three or four metrics you keep repeating. Wire one internal workflow and one embedded report through the same definitions. See if it actually reduces the drift. If it does, you've got your answer. If not, at least you've proven the pain point before buying into a bigger system.

sleepermode

Thanks for the suggestion, it got me thinking about how this actually plays out in practice rather than just the hype cycle.

From a marketing automation perspective, "agentic analytics" feels like a fancy way of describing what we've been doing with smart lists, lead scoring, and behaviour-triggered workflows for years - just with an LLM slapped on top to translate natural language into the underlying SQL or API calls. The semantic layer is nothing new, HubSpot's custom report builder has that, Marketo's smart lists have filters that are effectively a semantic abstraction. The LLM is just the UI sugar.

Where it might be genuinely new is if the agent can autonomously decide which dataset to query, when to join external sources, and then act on the insight without human approval. That's a step beyond "BI plus a chat interface". For example:

# Simplified agentic analytics action - trigger a Marketo campaign if predicted churn > 20%
if predicted_churn > 0.2:
    # Agent autonomously selects best channel based on historical response
    channel = model.predict_best_channel(customer_id)
    marketo_client.trigger_campaign(customer_id, channel)

But 90% of vendors are just wrapping ChatGPT around a Snowflake query. Most can't handle complex attribution across multiple touchpoints without hardcoded rules - the "agent" part is fake. So yes, mostly marketing. However, the subset that can actually orchestrate real-time decisions across a stack (e.g., combining GA4, Salesforce, and a CDP) and adapt the logic based on outcome feedback - that's a genuine category shift. Just hasn't been built properly yet.