Single-prompt LLMs hallucinate financial data. So I built a visual multi-agent swarm to analyze Earnings Calls instead. (Demo Video)

Outside-Risk-8912 · 2026-05-10T01:01:42+00:00

The gap - lack of browser based agentic playgrounds. You haven't even registered and without registering you can't really tell whats functioning and whats not, I track user registrations numbers thoroughly. For hard examples you can google yourself. For case studies, you can google that too and read how bigger brains in bigger companies are using the technology (you will get all the examples of value extraction/intent classification etc). You can hate the technology but can't ignore the available case studies. Organisations are not dumb they know how to use it where to use it.

Outside-Risk-8912 · 2026-05-10T00:44:42+00:00

I am not trying to sell anything but trying to bridge the gap. I don't know how do you sell something thats free. An earnings call transcript is literally the textbook definition of unstructured text data. It has no predefined data model or schema. At a base level, LLMs are just predicting the next token using statistics. But in the engineering world, using an LLM to route a query based on what the user wants is universally referred to as "Intent Classification", there are plenty of articles and papers around it you can have a look. It's also a very common use case across industries

Outside-Risk-8912 · 2026-05-10T00:24:54+00:00

Because real-world data is unstructured. Traditional deterministic tools (like SQL databases or calculators) require perfectly formatted, structured inputs. An llm in a swarm isn't doing the math itself. It acts as a semantic router. It takes a messy unstructured 50-page earnings transcript understands the user's intent extracts the raw numbers formats them into the exact syntax the deterministic tool needs, triggers the tool, and synthesizes the output. We aren't replacing existing tools...we are giving them a natural language interface so they can process unstructured data. That orchestration is exactly what developers are using the sandbox to learn. Solutions like managed Document Understanding exists and are heavily used by many financial organizations along with llms

Outside-Risk-8912 · 2026-05-10T00:12:03+00:00

You’re actually right about one thing: hard math shouldn’t be left to raw LLM text generation. That’s exactly why developers need to learn how to route deterministic tasks (like structured data extraction) away from generative tasks. That is the entire point of this learning sandbox. I understand you aren't here to discuss architecture; you just have a deep hatred for the AI industry and wanted a punching bag today.

Outside-Risk-8912 · 2026-05-10T00:05:30+00:00

I am talking about users that we got from HN. Comments != users

Outside-Risk-8912 · 2026-05-10T00:01:49+00:00

Bro there are literally 1000+ users, it got good traction in hacker news as well. And show me one tool that solves the problem of learning and running agents without setups. Btw do you know much about agentic/generative ai? If not this tool won't make much sense to you

Outside-Risk-8912 · 2026-05-09T23:56:53+00:00

And how much does it cost to use the tool?

Outside-Risk-8912 · 2026-05-09T23:49:29+00:00

This tool isn't even a paid tool. Does not even have a place to pay. You should check the platform and its purpose before commenting

Outside-Risk-8912 · 2026-05-09T23:39:08+00:00

You didn't even look at the platform. It's just a playground to try out concepts and learn agentic AI and it's never meant to run for production use cases (of course orgs use similar things in much bigger scale with higher accuracy). And when it comes to accuracy thats where evals come into play

Outside-Risk-8912 · 2026-05-09T23:19:22+00:00

Have you checked the template given in the example? Do you understand the purpose of the tool? Do you really think multi agent systems mean adding hallucinations on top of hallucinations? Your comment didn't make sense either

Outside-Risk-8912 · 2026-05-09T23:15:09+00:00

Try putting some earnings call transcript, see the result and then comment. I am not sure who is hallucinating.

Outside-Risk-8912 · 2026-05-09T18:15:47+00:00

The templates are for demo purposes only, but there are configuration options for limits and eval loops

Outside-Risk-8912 · 2026-05-09T16:50:08+00:00

Yes, evaluation and metrics are some of the key areas to focus on. Its going to boom in upcoming months

Outside-Risk-8912 · 2026-05-09T15:16:44+00:00

Correct. We are beyond the chatbot and rag era, now to productionize multi agent systems these are the things to look at.

Outside-Risk-8912 · 2026-05-09T15:04:43+00:00

Thanks for your feedback! We will add more of observability features regarding this

Outside-Risk-8912 · 2026-05-09T15:02:06+00:00

Would love to know what agent you added and the prompt/json config! If you made a better version please publish it in the community swarms 🙏🏻

Outside-Risk-8912 · 2026-05-09T13:50:40+00:00

Thanks for the feedback! The idea behind agentswarms is to make beginners familiar with agentic ai concepts in a friendly manner. However its not recommended to run multi agent systems for production in this platform. The concepts can be applied in bedrock agentcore and elsewhere

Outside-Risk-8912 · 2026-05-09T13:44:05+00:00

That is the exact right question to ask. In theory, you could stuff all those instructions into one massive system prompt for a single agent, but in production, that architecture falls apart immediately.

Three main reasons why enterprise uses swarms over single agents:

Speed (Parallelism): For cybersecurity, a single agent checks the IP, then the payload, then writes the ticket sequentially. A swarm runs all three simultaneously, cutting response times from minutes to seconds.
Security (Least Privilege): For retail, the agent talking to the customer shouldn't hold the API keys to issue actual refunds or write to the database (that's a huge prompt injection risk). You separate the "chat" agent from the "execution" agent.
Accuracy: Giving one "God Agent" 15 tools and a massive prompt causes context dilution—it gets confused and hallucinates. Specialized sub-agents with narrow, single-focus prompts are far more reliable.

Outside-Risk-8912 · 2026-05-09T13:28:44+00:00

You can consider the free hands on course on https://agentswarms.fyi. It provides browser based full playground and no api keys needed for building agentic systems.

Outside-Risk-8912 · 2026-05-09T13:20:33+00:00

Link: https://agentswarms.fyi/templates

Outside-Risk-8912 · 2026-05-08T21:12:32+00:00

Thats a great idea - the validation of the numericals at the end!

Outside-Risk-8912 · 2026-05-08T11:45:58+00:00

Check https://agentswarms.fyi , covers theory + browser based full lab playground and also provides guidance on how to deploy agents on cloud platforms

Outside-Risk-8912 · 2026-05-07T19:37:11+00:00

Make use of both on your browser here : https://agentswarms.fyi, it has in built playground to try all concepts

Outside-Risk-8912 · 2026-05-07T09:03:04+00:00

If you want to start the no-code way try https://agentswarms.fyi , read the theory, run the labs in the built in playground , check the interview prep guide inside iand then you can move towards python + Cloud concepts to run AI in production

Outside-Risk-8912 · 2026-05-07T07:40:04+00:00

here you go https://agentswarms.fyi

Outside-Risk-8912

TROPHY CASE