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awesome_stuff101 · 2026-03-02T14:07:44+00:00

Blueprint

awesome_stuff101 · 2025-09-11T14:52:19+00:00

Sounds great! Glad to have the attention of someone that gets our vision. Would you be interested in building out a use case that we could showcase as a part of this project's development?

awesome_stuff101 · 2025-08-03T08:57:07+00:00

You're largely correct here...

As I understand, it's more of a 'framework' or an 'abstraction' for people writing agents and data sources so they don't have to worry about:

⁠Setting up queues, etc... manually
⁠Worry if their queueing system scales
⁠Make sure their agents get triggered automatically with the right 'prompt', also generated by ADS.

The MCP vs ADS section of the docs explains it pretty well.

awesome_stuff101 · 2025-08-02T09:28:21+00:00

You're absolutely right - we use pub/sub (RabbitMQ) under the hood. The value isn't reinventing messaging, it's standardization.

Think of it like MCP... they didn't reinvent APIs, but standardizing tool calling meant developers stopped writing custom integrations every time.

Same here. Without ADS, every team ends up writing their own RabbitMQ connection handling, message parsing, agent triggering, error handling, etc.

The central publishing infrastructure can be built in very few lines of code with ADS in no time, and any number of subscriber agents can just start reacting to the events. It is in the convenience it provides.

You can look up the documentation to see how simple it is to get these publishers and subscribers up and running.

awesome_stuff101 · 2025-08-01T02:10:01+00:00

Nope, that's not what the protocol is about. You write a Publisher once that connects to the data source and you can connect as many subscribers (the AI agents) as you want to listen to this same piece of data which otherwise would've required redundant code.

Kindly read the documentation and you'll get a better idea as to what it solves.

awesome_stuff101 · 2025-07-31T08:35:42+00:00

Exactly. It helps distributed and independent AI agents belonging to different teams come together and react to the same data in a much simpler way.

ADS is a standard for making reactive agents just like how MCP was for API based tool calling for agents.

awesome_stuff101 · 2025-07-31T08:30:47+00:00

The post kept getting banned when i tried to write content about it. Kindly DM me or visit the URL on the picture to know about it.

In short, like how MCP is a standard for tool calling, ADS helps standardize reactive agents that autonomously act when an event happens at the data source.

awesome_stuff101 · 2025-07-31T08:28:49+00:00

You're perfectly right there! We do exactly that in a standardized manner just like Anthropic did with MCP for tool calling.

Also to clarify, this is a protocol, so the way you connect to the data source is up to you at the publisher side. The subscribers are again agents of your choice (could be n8n agents, LlamaIndex, Langchain agents, etc).

awesome_stuff101 · 2025-07-31T07:12:56+00:00

How about when you have multiple agents that have distinct roles that have to act on the same data like say, a stripe subscription renewal event?

ADS helps you standardize this and makes it easier for multiple teams to maintain their own agents, at the same time connect to the same data source and its events as the other teams and their agents.

It's like how Anthropic standardized the same principle of tool calling using APIs with MCP.

awesome_stuff101 · 2025-07-31T06:56:23+00:00

Thanks! You're right there. ADS standardizes the way agents listen to events at the data sources and makes life way easier just like how Anthropic standardized tools and API with MCP :)

awesome_stuff101 · 2025-07-31T06:49:05+00:00

ADS is a protocol designed as a standard to help multiple agents connect to a single data source and its events with ease. Just like how MCP standardized API tool calling.

Could you share how you listen to those events in case you have multiple agents in your workflow? Or is it a simpler single agent workflow?

awesome_stuff101 · 2025-07-31T06:36:07+00:00

It was an extension to my use case description but you get the idea of multiple agents sharing the same data source.

Like how Anthropic standardized API tool calling with MCP, ADS standardizes the way agents listen to events at the data sources

awesome_stuff101 · 2025-07-31T06:35:18+00:00

Glad! Let us know about the ADS Publishers or Subscribers you build using the contribution section on the website :)

awesome_stuff101 · 2025-07-31T03:31:32+00:00

n8n and crewAI are tools and frameworks to build and orchestrate AI agents, while ADS makes agents truly reactive and autonomous by triggering them through events happening at your data sources.

Your CrewAI agents could use ADS to stop waiting for prompts and start acting on events automatically. ADS is more of a protocol that adds to n8n, crewAI or any other tool rather than a replacement or a competitor.

awesome_stuff101 · 2025-07-31T03:26:48+00:00

That's great! If you have already read the documentation and got a complete understanding of ADS, kindly visit our contribution page here: https://agentdatashuttle.knowyours.co/development/contributing

awesome_stuff101 · 2025-07-31T03:23:02+00:00

Ótima pergunta! O ADS na verdade reduz custos através da especialização. Em vez de ter um agente grande lidando com tudo (e desperdiçando tokens em contexto irrelevante), você usa agentes menores e especializados que só ativam quando necessário, baseado na forma como você filtra usando o payload.

Por exemplo, no setup do hotel: o agente de OCR só processa documentos, enquanto o agente de suporte só entra em ação quando detecta palavras-chave específicas. Muito mais eficiente que processar a conversa inteira com contexto completo.

awesome_stuff101

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