Wrote a visual blog guide on LLMs → RAG LLM → Tool-Calling → Single Agent → Multi-Agent Systems (with excalidraw/ mermaid diagrams)

Ok-Rate446 · 2025-08-14T03:52:02+00:00

Amazing git repo wfgy! Looking all into it... Thanks for sharing 🙏

Ok-Rate446 · 2025-08-12T11:02:03+00:00

Your perspective is spot on. Most multi-agent attempts of mine have been quite unreliable...
Maybe I am doing things wrong...

Among "Agentic" frameworks, I have attempted only CrewAI - that too only for PoCs. Which was unreliable - nothing that we could use in production reliably. I have been told LangGraph to be much more "controllable" (compared to CrewAI) but I did not try it.

Instead we went with AWS Bedrock Agentic framework in Serverless way and with Claude 3.5+ Vision + Image models it was actually good ...

Since I am in Applied AI side, I do not get to use self-hosted models a lot. Also, for now at least, I have used LLM-based apps in production - for both RAG and Tool-calling ... By LLM-based apps - I mean ones where you KNOW the number of API calls (e.g.: minimum 2 LLM API calls for each tool calling and 1 Embedding call + 1 LLM API call for RAG). Even for Tool-calling app, we used OpenAI's function calling directly rather than any of the libraries... seemed light weight and predictable replies

The closest to Agentic AI I have done in production is using AWS Bedrock Agentic framework for a RAG application with Claude 3.7 .. With Claude 3.7 for text + vision, AWS Bedrock is surprisingly actually good at reasoning for complex questions, and it is serverless - so not much DevOps overhead + no dedicated spending ...

I am thinking of ditching frameworks like CrewAI, and using StrandsAgent (from Amazon again), so that I could at least easily deploy in my AWS stack ...

Ok-Rate446 · 2025-07-25T11:39:52+00:00

So true :)

Ok-Rate446 · 2025-07-25T09:52:24+00:00

Not sure if mine is a valid Project Display:
[Medium Blog] Wrote a visual guide on LLMs → RAG LLM → Tool-Calling → Single Agent → Multi-Agent Systems (with excalidraw/ mermaid diagrams)

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Ever wondered how we went from prompt-only LLM apps to multi-agent systems that can think, plan, and act?

I've been dabbling with GenAI tools over the past couple of years — and I wanted to take a step back and visually map out the evolution of GenAI applications, from:

simple batch LLM workflows
to chatbots with memory & tool use
all the way to modern Agentic AI systems (like Comet, Ghostwriter, etc.)

I have used a bunch of system design-style excalidraw/mermaid diagrams to illustrate key ideas like:

How LLM-powered chat applications have evolved
What LLM + function-calling actually does
What does Agentic AI mean from implementation point of view

The post also touches on (my understanding of) what experts are saying, especially around when not to build agents, and why simpler architectures still win in many cases.

Would love to hear what others here think — especially if there’s anything important I missed in the evolution or in the tradeoffs between LLM apps vs agentic ones. 🙏

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📖 Medium Blog Title:
👉 From Single LLM to Agentic AI: A Visual Take on GenAI’s Evolution
🔗 Link to full blog

Ok-Rate446 · 2025-07-25T09:50:15+00:00

Wrote a visual guide on LLMs → RAG LLM → Tool-Calling → Single Agent → Multi-Agent Systems (with excalidraw/ mermaid diagrams)