Stack view of how models, tools, orchestration, and product interfaces layer into shipped agents (PDF 241–242).
Router: A mechanism that directs tasks to the most appropriate agent or tool. Each of these terms forms a key piece of the agentic AI ecosystem that AI engineers should know. 4 Layers of Agentic AI The following graphic depicts a layered overview of Agentic AI concepts, depicting how the ecosystem is structured from the ground up (LLMs) to higher-level orchestration (Agentic Infrastructure).
Let’s break it down layer by layer:
1) LLMs (foundation layer)
At the core, you have LLMs like GPT, DeepSeek, etc. Core concepts here:
● Tokenization & inference parameters: how text is broken into tokens and processed by the model.
● Prompt engineering: designing inputs to get better outputs.
● LLM APIs: programmatic interfaces to interact with the model.
This is the engine that powers everything else.
2) AI Agents (built on LLMs)
Agents wrap around LLMs to give them the ability to act autonomously. Key responsibilities:
● Tool usage & function calling: connecting the LLM to external APIs/tools.
● Agent reasoning: reasoning methods like ReAct (reasoning + act) or
Chain-of-Thought.
● Task planning & decomposition: breaking a big task into smaller ones.
● Memory management: keeping track of history, context, and long-term info. Agents are the brains that make LLMs useful in real-world workflows.
3) Agentic systems (multi-agent systems)
When you combine multiple agents, you get agentic systems. Features:
● Inter-Agent communication: agents talking to each other, making use of protocols like ACP, A2A if needed.
● Routing & scheduling: deciding which agent handles what, and when.
● State coordination: ensuring consistency when multiple agents collaborate.
● Multi-Agent RAG: using retrieval-augmented generation across agents.