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The biggest miss in a lot of ai projects is treating these like buzzwords instead of layers. Once you split them, the stack makes sense.

Insightful 💡, sharing with our network 🛜 Luís Rodrigues

Solid analogy. Makes these concepts way easier to explain to non technical folks.

This anatomy breakdown is exceptionally clean, Luís. Mapping MCP to the nervous system and agents to the hands perfectly conceptualizes how these layers interact. You hit the exact nerve when you pointed out that if permissions are loose, the hands touch things they shouldn't and complete the wrong tasks fast. The industry is realizing that a brain with hyper-fast nervous system wiring but zero physical restraint is an immense operational hazard. To make this 'body' enterprise-ready, it needs a skeleton. When the nervous system (MCP) carries an execution impulse to the hands (Agents), you cannot rely on the brain's internal prompt filters to stop an unauthorized mutation. The containment must be mechanical. True runtime governance acts like a localized reflex arc. Before the hands can modify a database or trigger a transaction corridor, the underlying architecture must enforce an un-bypassable Readiness Hold. If the cryptographic mandate isn't instantly verified at the point of impact, the joint locks, the execution circuit breaks, and the system fails closed. We have to stop trying to reason with the brain and start anchoring the physical limits of the body.

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Very interesting. RAG could also be Brain + Focus.

Excellent analogy. One of the biggest challenges in AI discussions today is that people mix infrastructure, intelligence, retrieval, and orchestration into the same bucket. This visual simplifies the stack in a way both technical and business leaders can understand quickly.

LLMs think. Agents act. MCP connects. But autonomous systems may ultimately require something else: Continuous governability.

Luís Rodrigues Great breakdown. One thing I would add: in enterprise AI, the weakest layer is often not the model. It is the system around it. LLMs, RAG, Agents, and MCP only create value when data quality, permissions, governance, ownership, and decision flows are designed intentionally. Agentic AI is powerful, but without the right operating model, it can simply automate confusion faster. The real work is designing the whole system.

The 'body' analogy is perfect Most people are still trying to build with just the 'brain' (LLM) and wondering why it’s not delivering business results. The shift to agentic workflows—where the system actually has the hands to execute—is the real game changer Crucial breakdown

Useful framework. Most “connected” systems only reveal misalignment when identity, permissions, and definitions are tested in execution.

Great analogy

The organizations that adapt best will likely be the ones helping people understand AI practically instead of making it feel unnecessarily complex.

Brilliant framing. The biggest insight here is that enterprise AI breaks when the layers are treated as separate projects instead of one system.

Great way to present that AI is not just two letters

Luís Rodrigues This is a useful way to frame the AI stack. LLMs, RAG, agents, and MCP - each solve a different problem, but enterprise value only appears when they are designed together around workflows, controls, and business outcomes. Deepesh Khandelwal, MBA, PMP, SA

Great schematic. Thankfully we are using all these in SENSEI - https://sensei.scmdojo.com/agentic-workflows

Beautiful analogy. Thanks for sharing Luís.

MCP is more like a tool store or a garage with tools

LLMs don't "reason", they generate tokens based upon the next most likely token.