Understanding AI Agents: Core Components, Architecture, and Practical Implementation

What is an AI Agent?

AI Agent is an application that observes its environment, uses tools, and takes actions to achieve specific goals.

Key characteristics

Autonomy – operates without direct human intervention.

Goal‑oriented – designed to accomplish defined objectives.

Proactiveness – can reason and decide next steps even without explicit commands.

Broad applicability – especially for generative AI models.

Basic agent architecture:

Core components of generative AI Agents

Model (The Model)

The model is the “brain” that performs reasoning and decision‑making, often built from large language models such as GPT‑3 and using frameworks like ReAct, Chain‑of‑Thought (CoT) or Tree‑of‑Thoughts (ToT). Models can be fine‑tuned for specific tasks.

Tools (The Tools)

Tools enable the agent to interact with the external world, extending capabilities beyond pure language modeling. Common tool types include:

Extensions – standardized API bridges for seamless execution.

Functions – model‑generated function calls executed by the client.

Data Stores – databases that provide dynamic, up‑to‑date information.

Example of extensions:

Example of functions:

Example of data stores:

Orchestration Layer (The Orchestration Layer)

The orchestration layer coordinates the model and tools, handling memory, state, reasoning, planning, and prompt engineering. It may implement frameworks such as ReAct, CoT, or ToT to guide the agent’s actions.

Agent‑Tool collaboration

The three components work together like a chef: gathering ingredients (information), reasoning about the recipe, performing actions (cooking), and adjusting based on results.

Differences between models and agents

Models rely solely on training data and cannot manage multi‑turn dialogue or use built‑in tools, whereas agents can invoke external tools, maintain conversation state, and execute complex tasks.

Core concepts of agent operation

Agents iterate through information collection, internal reasoning, action execution, and adjustment, with the orchestration layer acting as the “executive brain.”

Enhancing model capabilities

In‑context learning

Provides prompts, tools, and few‑shot examples at inference time, enabling real‑time adaptation (e.g., ReAct).

Retrieval‑augmented in‑context learning

Retrieves relevant external knowledge to enrich prompts.

Fine‑tuning

Trains the model on task‑specific datasets before inference.

Practical implementation on Google Vertex AI

Vertex AI offers a managed environment for production‑grade AI agents, including natural‑language specifications, tool integration, and components such as Vertex Agent Builder, Extensions, Function Calling, and Example Store.

Example Vertex AI agent architecture:

Conclusion

Generative AI agents combine models, tools, and an orchestration layer to extend language models, access real‑time information, and autonomously solve complex tasks. Techniques like ReAct, CoT, and ToT support reasoning, while tools such as extensions, functions, and data stores provide the bridge to the external world.