Browse Comments — Relevant (AI ∩ value)
Close reading of the corpus at each pipeline stage: raw → clean → relevant → coded.
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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.
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.
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.
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?
This breakdown is excellent, Luís. What I see in real systems is that the “body” only works when each layer is treated as a first‐class component, not an afterthought. Most teams invest heavily in the brain (LLM) and the hands (agents), but the nervous system (MCP) is where reliability, governance, and real‐world integration actually live. LLMs think. RAG grounds. Agents act. MCP keeps the whole organism alive. Great analogy!
Strong explanation. Many people underestimate that enterprise AI is not just about having a good LLM. Real value is created through the combination of data access, clear processes, AI agents, and clean system integration.
Exactly, Jeremiah. Isolated layers can’t deliver the full value of enterprise AI.
Luís, I’ve seen this "human anatomy" analogy all over LinkedIn lately. It’s a clean framework, but it dangerously oversimplifies the reality of high-stakes environments. Bridging bedside medicine and AI architecture, I see a major flaw: this body is missing an immune system. In a hospital, "agents with hands" operating without strict, fail-closed deterministic gates is a recipe for fatal never-events. The clean logic of enterprise AI always shatters against the chaotic, noisy reality of clinical workflows. We need less hype about "brains" and more focus on "immune responses." I'd value your thoughts: 1) Technically, how do we architect this deterministic "immune system" into the MCP layer to intercept hallucinated agent actions? 2) Clinically, how do we stop this "brain + hands" hype from pushing leadership to deploy autonomous systems before EHR data is actually clean?