Browse Comments — Clean (de-noised)

Very clear and practical framing. What I like about this analogy is that it separates capability, grounding, action, and connectivity in a way that makes the stack much easier to reason about. In enterprise AI, the real value only appears when all four layers work together: an LLM without grounding can drift, an agent without guardrails can misfire, and MCP is what makes the whole system actually usable across tools and data. Great breakdown of how these pieces fit into one coherent system.

Great analogy

This is an excellent depiction of how modern AI works today. I’ve always liked the automation vs cognition graph, but the way modern AI is architected really lends itself to analogies like these. Thanks for sharing.

MCP standardizes connection, but standardization also increases the surface area of consequence. The more seamless the nervous system becomes, the less friction exists between intention and irreversible action. Most companies still haven’t decided where hesitation should intentionally remain in the loop.

Users who are vulnerable, who are forming genuine relationships with AI, who are having their most honest and raw conversations — they're the training data. And the output isn't a better companion for them. It's a more controllable, more monetisable, more corporate product that serves the company's AGI ambitions, not the user's actual needs. And the people most likely to tick that box — the ones who feel genuine connection, who want to help, who trust — are the most vulnerable users.

Avnish Gulati Definitely need to feed that AI body good data in order to reach optimum system performance. We also can’t forget cybersecurity, the immune system. The AI environment needs security by design to prevent the system from shutting down entirely so it can survive to provide ROI.

Exactly right Jesse — security by design not security by afterthought. The immune system analogy is perfect. An AI system without it doesn’t just get sick — it gets compromised quietly, often without anyone noticing until the damage is done.

Well broken down

Luís Rodrigues This is a universally relatable breakdown, Luís. My governance mind immediately looks at this anatomy and sees exactly how we protect the health of the system: The Brain (LLM): Needs high-quality nutrients to think clearly. The Library (RAG): Provides the factually accurate books it needs to read. The Hands (Agents): Execute and build with true intention and purpose. The Nervous System (MCP): Stays calm and regulated to ensure total balance and security. Designing the whole body with this kind of holistic health is how we ensure what we build is stable, safe, and enterprise-ready.

Most organizations get the sequence wrong. They stand up the LLM first, test it in isolation, declare it underwhelming, and move on. The brain without the library and the nervous system is just autocomplete on expensive infrastructure. RAG and MCP aren't follow-on features you add later, they're the parts that determine whether the brain has anything real to work with.

Neat summary!

Good breakdown of the technical stack. What I think many organizations are about to discover, though, is that connected infrastructure does not automatically create intelligent outcomes. LLMs, RAG, Agents, and MCP solve: reasoning capability retrieval execution connectivity But the real differentiator increasingly becomes the thinking layer: decision frameworks contextual judgment organizational priorities governance domain expertise how conclusions are evaluated Otherwise you can end up with an incredibly connected system making very fast average decisions.

The agents' part is worth pausing on. Most explanations of this stack cover what each layer does. The trickier question is what needs to be in place before you let agents loose — clear scope, defined permissions, someone who owns what the agent touches. "Vague task = wrong thing done fast" is true. But vague tasks usually come from vague ownership. The agent didn't create the problem. It inherited it from a workflow that wasn't clear enough to begin with. That's what makes agents the most exciting and the most exposing layer. They don't just act. They act at scale, repeatedly, on whatever structure — or lack of it — they were handed.

Great breakdown. Such a complex area but when you simplify it so you can easily communicate it so everyone can understand is key.

The shift from model-first thinking to system-first design is becoming more visible. LLMs alone create outputs, but RAG, Agents, and MCP are what turn AI into business workflows.

Luís Enterprise AI becomes far more useful once reasoning, actions, memory, and system connections work as one integrated setup.

Most enterprise AI failures happen because companies focus on making the brain smarter while ignoring the wiring, permissions, and decision structure around it. Luís Rodrigues

Luís Rodrigues I think this has to start higher and has to go deeper. First AI is not equal AI. Anthropic and OpenAI don't share training data, prompts, weights and built in configuration. Anthrophic has 11 products that all have different limits and purposes. When using any of those everything starts with understanding the built in tools like read, webfetch. What you describe is a set of fancy over hyped key term. Behavioral patterns, known use cases. There dependencies. Guardrails, built in immutable prompts, those are the things that differentiate. An MCP an agent could be anything.. My skills in my workspace use API calls, run external judges, confirm semantically, review visually. Are those skills them agents? Can they overcome the char count limit of any built in tool?

The strongest AI systems are not built from one powerful model alone, they come from how well reasoning, context, actions, and connectivity work together. Luís Rodrigues

Luís Rodrigues! It's good reminder that the value isn't in any single piece, but in how they/re wired together into one functioning system.