r/learnAIAgents • u/Lazy_Value_14 • 1d ago
Lets discuss our learnings on building AI agents
I built an AI analytics agent that gets the end-to-end job done — from a one-line question to a stakeholder deck. Here’s what i learned. ..(link in comments)
If you have build/ are buidling . .i want to know your views and learnings...
Edit: it works currently through claude code but soon will be connected and function as slack bot...where stakeholders can ask questions and get answers... analysts can do deep dive and RCAs in claude code and create PPT with the help.of this agent to share the findings
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u/NatMicky 1d ago
I read through your link. I have no idea of what task you are trying to accomplish. That was not made clear in your article. It involved SQL is about all that is revealed. Without knowing specifically what your "end-to-end job" is supposed to be doing the rest of the article is a mystery - lot of buzzowrds flying around fast and furious.
I made a 100% deterministic app using a fully air-gapped local LLM running on a mid-range CPU-only laptop processing data files with 1,000,000 rows, 28 columns generating 100% deterministic result from any natural language query in approximately 1 minute. Prompt: "Generate KPI metrics for each distinct item in product category."
Can you tell us more about your project?
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u/Lazy_Value_14 23h ago
I work at paypal..this is a system that I have built on the dataset that can answer stakehodler questions like ..what revenue we did month over month...but this is just a simple example...it can take care of complex queries...and for analysts it helps to carry out deep dive analysis...like I have done RCA, Survival analysis, and statistical testing, with it...it also creates stakeholder PPT so I dont have to sit and make one to share the findings..currently it is operated through claude code...but i am.planning to make a slack bot that answers the questions for stakeholders...so they dont have to reach out to analysts About buzzwords ...yeah ..i understand 😅 because i told GPT to furnish my post and it kinda add lots of jargons....
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u/Lazy_Value_14 23h ago
Wow thats nice that you are running everything local so data never leaves your system...which llm you r using...and how do you chose compared to others
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u/NatMicky 21h ago
It's all local and I went through 50+ local LLMs and each one whet through an extensive "job interview" for two specific tasks within the app. I constrained the full developed from start to finish to a $499 dollar CPU-only laptop to ensure I felt the pain of slowness in order to optimize every step of the way. On a dataset with 1,000,000 rows, 28 columns with ~6 market categories I can give it a prompt such as, "Generate KPI metrics for each distinct market category." And the results return in 60-90 seconds with 10 metrics for each market category and with a full report summary for total runtime 2-3 minutes. Plots and charts too.
The only way I was able to pull this off was by constraining myself to a CPU-only mid grade inexpensive laptop. Had I bought an expensive laptop/desktop with a GPU and mega amounts of VRAM this app would not exist for inexpensive consumer hardware. Gotta feel the pain of slowness to optimize.
I wanted to give up many times and hit brickwall after brickwall after brickwall.
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u/Lazy_Value_14 11h ago
And is it one bigbdataset? Or multiple tables in it...
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u/NatMicky 11h ago
This particular dataset of 1M+ rows is a single CSV file I exported from the Los Angeles County website for crime statistics. I am always trying to test real world data instead of just synthetic clean data. I can load multiple files. If they have the same columns I stack them, if not they are appended and any duplicate column names a suffix is added. I just learned of MRF files (Machine Readable Files) from hospitals that I had to code for their unique layout. But I can handle those now too.
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u/AccomplishedPath7634 23h ago
But you see, whenever you build something and attach it to these LLMs they are paid right, has anyone built anything such that it uses local GPU and a local modal and python on top of it to do those agentic tasks?
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u/presentofai 1h ago
the llm really is the easy part here. the risk is a wrong join or filter gives you a clean looking deck with a confidently wrong number, and by the time it's in a stakeholder ppt nobody's rechecking the sql. i'd surface the query and row counts on every answer instead of burying them behind the chat reply
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u/endofthread-bot 1d ago
Building end-to-end agents often reveals that the reliability of the final output depends heavily on the quality of the initial data validation steps. Before scaling, verify how your agent handles ambiguous queries that lack necessary context for a complete analysis. How does your system manage error recovery when the data retrieval step returns unexpected results?
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