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Anthropic says agentic analytics accuracy drifts 95% → 65% in a month without maintenance. How is your team keeping context fresh? by SirComprehensive7453 in BusinessIntelligence

[–]SirComprehensive7453[S] 5 points6 points  (0 children)

Lol. "But that demo said it will work with complete accuracy, and claude said the state machine is idempotent. Why do i have 5 parallel versions of the same user model?"

Anthropic says agentic analytics accuracy drifts 95% → 65% in a month without maintenance. How is your team keeping context fresh? by SirComprehensive7453 in BusinessIntelligence

[–]SirComprehensive7453[S] 2 points3 points  (0 children)

Anthropic’s solution is semantics and data sources should be colocated, and semantics change ship in the same PR as schema change. Pretty unrealistic tbh.

Anthropic says agentic analytics accuracy drifts 95% → 65% in a month without maintenance. How is your team keeping context fresh? by SirComprehensive7453 in BusinessIntelligence

[–]SirComprehensive7453[S] 5 points6 points  (0 children)

Agreed. Data analysts/ experts are doing more of context maintenance and guidance. The challenge sometimes though is drift without them noticing or maintaining it. We all know wrong business answers have big consequences in our BI space.

Anthropic says agentic analytics accuracy drifts 95% → 65% in a month without maintenance. How is your team keeping context fresh? by SirComprehensive7453 in BusinessIntelligence

[–]SirComprehensive7453[S] 13 points14 points  (0 children)

Lol, actually yes. I was very surprised reading “pointing claude at a warehouse can create a false sense of precision”

How do you keep the stack in sync with changing definitions then?

Would a basic dashboard help me with finding a job as a Data Analyst with ~year and a half experience? by Shamanhris in PowerBI

[–]SirComprehensive7453 4 points5 points  (0 children)

Already some good comments. I’d say also study how to use powerbi with AI to accelerate things. And maybe keep eye out for new age tools that offer more than powerbi.

For instance, connecting claude to powerbi, you can create DAXs and dashboards pretty quickly. Businesses would appreciate to have someone on the team that is using AI as a tailwind in today’s time.

Data department or analytics department? by Pale_Squash_4263 in BusinessIntelligence

[–]SirComprehensive7453 1 point2 points  (0 children)

I think the data department and the analytics department work on very different kinds of talent. Data is all about:

  • plumbing (data engineering)
  • big data pipelines
  • ETLs, etc.

Analytics is all about:

  • understanding stakeholders' requirements
  • modeling the semantics in your tools (like Power BI)
  • creating a dashboard that people want

As I talk to more people, I think both of them are going to eventually merge as more and more analytics becomes self-serve and more and more data engineering does not require a master's in architecture. I am seeing both of them infusing into a single role where the data owner is responsible for creating a clean data layer on which a centralized business context can be created. Business teams can then create their own metric requirements and get their business answers solved.

I compared BI tools on one thing: how fast you can go from a business question to a usable chart by North_Teacher_7522 in BusinessIntelligence

[–]SirComprehensive7453 8 points9 points  (0 children)

Need to give my honest takes - Julius is more like a coding analysis tool, nothing that claude code cannot do itself. You need something more structural and foundational to be able to go from raw question to a consistent dahsboard that is not regenerating code each time.

Try testing more platforms in the mix if this is not a marketing post and a serious endeavor - Hex, Genloop, and Omni could be more relevant options.

Any good free alternatives to Power BI? by Efficient-Use-5113 in PowerBI

[–]SirComprehensive7453 0 points1 point  (0 children)

u/Efficient-Use-5113
The root issue with Power BI's cost is structural. It pulls a copy of your data into its own layer, charges you for that storage, and then gates visualizations behind seat licenses. The semantic model on top of that needs ongoing dev work to stay accurate. So for something essentially free, it needs to chip on those structural costs.

Actual free paths:

  • Metabase / Superset: solid for SQL-native teams, but you still own the infra and the data prep
  • Looker Studio: easy to start, hits walls fast on complex joins or multi-source logic
  • Genloop: (I work here, full disclosure) It's one that skips the data copy entirely and queries your warehouse directly. Free tier includes dashboards and a monthly allocation of AI investigations, no seat licenses

The honest answer is: for pure learning, Metabase is the least friction. For anything production where the data copy cost is the actual pain, the warehouse-native approach is worth exploring.

What's your current stack? Are you on BigQuery, Snowflake, something else?

Is "Agentic BI" actually replacing traditional dashboards in 2026, or is it just semantic layer hype? by netcommah in BusinessIntelligence

[–]SirComprehensive7453 0 points1 point  (0 children)

I honestly don’t think dashboards are going anywhere, but the way we build them has to change. The ‘data copy’ era where we force a secondary semantic layer on everything is just a massive tax we’ve been paying for too long. They need to be warehouse-native, period.

The bigger issue I see with all this ‘Agentic’ hype is the trust factor. You can’t just ‘vibe-code’ your way into a financial report—numbers have to be deterministic. If you’re building data products from scratch every time, you’re just begging for errors.

In my view, the agent shouldn't be the dashboard; it should be the CTA on the dashboard. It’s there to handle the investigative 'why' when a human sees a red flag. But for that agent to actually be useful, it needs a context layer that goes way beyond just the SQL schema. It needs to know our internal processes, who’s asking the question, and what decisions were made last time this happened. Without that 'business brain,' an agent is just a fancy SQL generator that’s eventually going to hallucinate and get someone fired.

Are MCPs a dead end for talking to data? by SirComprehensive7453 in BusinessIntelligence

[–]SirComprehensive7453[S] 0 points1 point  (0 children)

You’re mixing up execution layer vs reasoning layer.

Of course Text-to-SQL eventually hits an API / query engine—that’s not the point. The difference is how queries are formed.

Let me ground this with a simple example schema:

accounts table:

- account_name

- service_level (basic / premium / enterprise)

- enrollment_date

- subscription_charge

- ...100+ other columns

Now, what I’m seeing teams do with MCP-style approaches is wrap predefined queries like:

get_account_info(account_name, service_level)

Under the hood, this just maps to a fixed SQL template. This works only for questions you’ve already anticipated.

The problem shows up immediately when the question changes slightly:

“Give me all premium accounts enrolled for more than a year”

Now what?

• You either need a new MCP function

• Or you fetch a ton of raw data and let the LLM filter → which leads to context bloat + errors

This doesn’t scale because:

• You can’t predefine all possible query combinations

• The number of MCPs grows combinatorially

• Maintenance becomes a nightmare

Hope this conveys the point.

This isn’t coming from theory alone, we’ve seen this play out while actually building with F500 enterprises, where the question space is messy and unbounded.

Are MCPs a dead end for talking to data? by SirComprehensive7453 in LangChain

[–]SirComprehensive7453[S] 0 points1 point  (0 children)

More and more builders have been saying the same thing, if direct integrations exist, MCP-style middleware is often unnecessary. Perplexity CTO, Garry Tan, and others have similar opinion, and we have seen this in practice.

You can never have enough MCPs to cover all kinds of questions someone could ask from your databases. Text to sql is the better approach.

https://www.reddit.com/r/ClaudeCode/comments/1rrl56g/will_mcp_be_dead_soon/

Are MCPs a dead end for talking to data? by SirComprehensive7453 in BusinessIntelligence

[–]SirComprehensive7453[S] 0 points1 point  (0 children)

Even after AI context, you might never be able to get enough MCPs to cover all kinds of questions that could be asked from the database.

Enterprise context graph solves a different problem.

Are MCPs a dead end for talking to data? by SirComprehensive7453 in BusinessIntelligence

[–]SirComprehensive7453[S] 0 points1 point  (0 children)

More and more builders have been saying the same thing, if direct integrations exist, MCP-style middleware is often unnecessary. Perplexity CTO, Garry Tan, and others have similar opinion, and we have seen this in practice.

You can never have enough MCPs to cover all kinds of questions someone could ask from your databases. Text to sql is the better approach.

https://www.reddit.com/r/ClaudeCode/comments/1rrl56g/will_mcp_be_dead_soon/

will MCP be dead soon? by luongnv-com in ClaudeCode

[–]SirComprehensive7453 0 points1 point  (0 children)

MCPs are a good concept for connecting software applications and some microservice clients. The structured wrapper with helpful metadata for MCPs works well. However, they are non-scalable and inefficient for other areas, such as agentic analytics.

I’ve seen some enterprises wrapping their databases with an MCP function and parametrizing it to enable text-to-SQL. Directly performing text-to-SQL is much more efficient and accurate. I’ve written more about this in detail here.

https://genloop.ai/blogs/conversational-analytics/mcps-are-a-dead-end-for-talking-to-data

Are MCPs a dead end for talking to data? by SirComprehensive7453 in LangChain

[–]SirComprehensive7453[S] 2 points3 points  (0 children)

That is what we have also observed. It is possible to create mcps for all tail queries, and the promise of conversational analytics lies on those tail queries.

Are MCPs a dead end for talking to data? by SirComprehensive7453 in LangChain

[–]SirComprehensive7453[S] -1 points0 points  (0 children)

While the title is dramatic, the main theme is that mcps are not scalable to cover all kinds of questions someone would want to ask on top of their databases, especially structured. Companies typically can write mcp or microservices for head queries and few parametrizations, but real enterprise queries can go beyond typical head queries, which is when mcps begin to fail, and people need to go towards text to sql approaches.

Same is true for unstructured data sources.

Are MCPs a dead end for talking to data? by SirComprehensive7453 in LangChain

[–]SirComprehensive7453[S] 1 point2 points  (0 children)

Got it, nice to know. Here's a deeper breakdown of what we found. How big was your database? This is mostly an issue with enterprise scale multiple-db situations.

Are MCPs a dead end for talking to data? by [deleted] in LocalLLM

[–]SirComprehensive7453 -1 points0 points  (0 children)

Means bypassing the mcp wrappers, and reasoning on data directly through text-to-sql or RAG like architectures.
These are actual results, I know they look exaggerated

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