Browse Comments — Clean (de-noised)

RAG as the library providing real-world context transforms theoretical AI capabilities into practical business tools. Many organizations are discovering this crucial distinction between training knowledge and operational knowledge availability.

Thanks Ruben Hassid 👍

The acknowledgment that vague agent tasks lead to fast incorrect completions is important. This challenge requires robust task specification frameworks and careful governance before deploying autonomous capabilities at scale.

So true, Janelle. Treating these layers as separate tools is where most strategies break down.

Is one of the company plants in Holdrege,NE,

AI systems work best when LLMs provide reasoning, RAG adds context, agents execute actions, and MCP connects everything into one usable system. Luís Rodrigues

The agents point is the one most organisations underestimate. Vague goals and loose permissions is a bad combination at any speed.

The standardization value of MCP connecting disparate systems cannot be overstated. Without this integration layer, even sophisticated AI systems remain isolated tools rather than cohesive enterprise intelligence platforms.

Ruben Hassid agree with you!

Ruben Hassid Stop wasting hours on long, over-complicated tutorials, if you aren't actively training AI to speak in your exact voice, pushing back on your logic, and running your workflows on autopilot, it is still just a chatbot to you instead of a true strategic partner.

Stacking small automations doesn’t feel like a big deal at first, but after a few days you realize you’ve stopped doing half the repetitive work yourself.

This explanation makes complex infrastructure easy for busy executives.

Luís Rodrigues - the body analogy works. To all the leaders working through AI adoption, just as we humans use different parts of the body as needed, we should do the same for our AI use cases. Brain will always be needed, and we should use library, tools and MCP on need basis. Often it will be a mix of these that will be fit for purpose.

My experience is a literature professor: this was happening, Long before AI.

I used Claude to create a skill that looks through Smartlead for campaign results and Hubspot for conversions, then create a report of my outbound campaign performance every week, and what would have taken me at least 30 minutes every week to put together was built in 30 minutes and runs automatically on schedule every week. Now I am imagining how many more things are possible with it

Dont have the energy to dig into the detail of this 'aspiration' however as always the devil will be in there

It couldn't be any simpler than this. I'll definitely share to my network.

Thinking, grounding, acting, and connecting form a powerful framework for enterprise AI maturity assessment. Organizations can evaluate their progress across each dimension and identify specific gaps requiring investment and attention.

Ruben Hassid This is the AI adoption gap in miniature. People don’t fail because they lack access to the model. They fail because they never give it context, standards, examples, files, permissions, or repeatable work to own.

The library concept within RAG deserves emphasis in every AI conversation. It represents the bridge between what models learn generally and what organizations need specifically from their unique data and operational context.