Deconstructing Agentic AI: Luís Rodrigues Breaks Down the 5 Layers of Autonomy

In a recent LinkedIn post, Luís Rodrigues offers a clear breakdown of Agentic AI, a concept that is gaining significant traction but is often misunderstood. Rodrigues presents a layered mental model to demystify the progression from basic machine learning to sophisticated autonomous systems, emphasizing that mastery of foundational elements is key to understanding the more advanced layers.

Rodrigues begins by outlining the fundamental layers, starting with Machine Learning. He explains this layer as:

“Feed a system labeled examples. It learns patterns. Then it predicts labels for new data. Classification, regression, clustering.”

According to Rodrigues, this foundational layer powers many current applications, including recommendations, fraud detection, and search ranking.

Understanding the Building Blocks of Agentic AI

Building upon machine learning, Luís Rodrigues details the subsequent layers, highlighting how each enables more complex AI capabilities. The second layer, Deep Learning, is characterized by its ability to automatically identify relevant features without manual engineering. Rodrigues points out that this is crucial for understanding unstructured data.

He elaborates on the significance of Deep Learning:

“Neural networks figure out which features matter. No hand-engineering needed. Give it raw text, it converts meaning into vectors (embeddings). Embeddings are why semantic search works.”

Following this, Rodrigues introduces Generative AI as the third layer. This layer is defined by its ability to predict the next element in a sequence, which, when scaled, leads to models capable of understanding language, reasoning, and code. The attention mechanism, as Rodrigues notes, allows these models to focus on the most relevant parts of the input.

From Generation to Action: The Emergence of AI Agents

The post then transitions to the concept of AI Agents, which Rodrigues defines as Generative AI that is empowered to act, not just communicate. This layer introduces critical functionalities such as function calling, Retrieval-Augmented Generation (RAG) to mitigate hallucinations, the ReAct framework (reason, act, observe, repeat), and memory to maintain context across interactions.

Luís Rodrigues emphasizes the distinction of this layer:

“GenAI that acts, not talks. → Function calling: the model picks which tool to use → RAG: retrieves real data instead of hallucinating → ReAct: reason, act, observe, repeat → Memory: holds state across turns”

This active capability is a significant leap from purely generative models.

The Apex: Agentic Systems and Coordination

The highest layer discussed by Rodrigues is Agentic Systems, which involve the coordination of multiple AI agents working towards shared objectives. This layer encompasses advanced capabilities like planning (decomposing goals into subtasks), efficient handoffs between agents, persistence of memory across sessions, and self-correction mechanisms to handle failures.

Rodrigues highlights the complexity and potential of this layer:

“Multiple agents with shared objectives. → Planning: decompose goals into subtasks → Handoffs: one agent delegates to another → Persistence: memory that survives across sessions → Self-correction: detect failures, retry a different way → Human-in-the-loop: escalate when the stakes are high”

He concludes by reiterating the hierarchical nature of these layers, stating that each subsequent layer is built upon the foundations of the ones below it. This perspective underscores the importance of understanding the core AI technologies before delving into the more complex applications of agentic systems.