Essential Components of an AI Agent Architecture

Agent Architecture Overview

An AI agent processes inputs, reasons with an AI model, and takes actions using a set of tools, optionally leveraging memory to retain context and learn from interactions. The goal is to build an autonomous system that understands intent, plans multi‑step actions, and executes them via available tools.

Frontend Framework

Frontend frameworks provide pre‑built UI components, libraries, and tools. Two categories are recommended:

Prototype and internal‑tool frameworks : Favor rapid development and simple request‑response models (e.g., Mesop, Gradio). Suitable for demos and quick testing but may lack real‑time interaction support.

Production frameworks : Support streaming protocols, stateless APIs, and externalized memory for robust multi‑user applications (e.g., Streamlit, React, Flutter AI Toolkit).

Communication between frontend and the AI agent can follow the open Agent‑User Interaction (AG‑UI) protocol, often combined with the Agent Development Kit (ADK).

Agent Development Framework

Development frameworks abstract common agent functions—reasoning loops, memory, and tool integration. Google Cloud’s open‑source ADK offers modular components optimized for Gemini models but works with other models and runtimes. ADK includes Python, Java, and Go examples across various domains and supports multi‑agent coordination via shared session state, model‑driven delegation, and explicit function calls. For finer‑grained control, generic AI frameworks like Genkit can be used.

Agent Tools

Tools extend an agent’s capabilities beyond text generation, enabling complex multi‑step tasks. Three usage modes are described:

Built‑in tools : Quick start for common tasks such as web search or code execution.

Model Context Protocol (MCP) : Enables modular, reusable toolsets for multi‑agent systems.

Custom function tools : For integrating proprietary or third‑party APIs without an MCP server.

When selecting tools, prioritize observability, debuggability, and robust error handling.

Agent Memory

Short‑Term Memory

Maintains context within a single conversation. Options include:

In‑memory storage : Simple key‑value structures suitable for single‑instance development; state is lost on restart.

External state management : Stateless design that stores session data in services such as Memorystore for Redis, Firestore, or Vertex AI Agent Engine sessions. ADK’s DatabaseSessionService requires a relational database like Cloud SQL.

Long‑Term Memory

Provides a persistent knowledge base across users. Options include:

In‑memory storage : Used for testing via ADK’s InMemoryMemoryService.

External persistent storage : Managed services on Google Cloud that ensure durability and scalability.

Agent Design Patterns

Design patterns guide component organization, model integration, and workflow orchestration. Single‑agent systems rely on one model for reasoning, planning, and tool selection—ideal for early prototypes. Multi‑agent systems coordinate specialized agents, improving scalability and reliability but adding access‑control, orchestration, and cost considerations. The Agent‑to‑Agent (A2A) protocol facilitates communication between agents.

AI Model

Gemini Pro is recommended as the primary model for agent reasoning. Managed APIs provide the latest proprietary models with minimal operational overhead, while self‑hosted models offer fine‑grained control for strict security or data residency needs. Model routing can direct simple requests to smaller language models and reserve larger models for complex tasks, balancing performance and cost.

Model Runtime

Choosing a runtime depends on the deployment scenario:

Vertex AI : Fully managed API for Gemini, partner, and custom models with enterprise security and scalability.

Cloud Run : Serverless, event‑driven containers for open or custom models, emphasizing simplicity and cost efficiency.

GKE : Provides maximum infrastructure control for containerized models, suitable for complex security or networking requirements.

Additional options such as Gemini API and Compute Engine are mentioned for specific use cases.

Agent Runtime Environment

The agent runtime executes the business logic written with the development framework and calls the selected model runtime. Selection guidance:

Vertex AI Agent Engine : Fully managed Python agents with low operational overhead.

Cloud Run : Containerized agents needing serverless, event‑driven scaling and language flexibility.

GKE : Containerized agents with complex state requirements and fine‑grained infrastructure configuration.

Vertex AI Agent Engine offers built‑in managed memory, supports MCP and A2A protocols, provides observability via Google Cloud Observability, and includes security features such as service identity, sandboxed code execution, CMEK‑protected secrets, and IAM‑based network controls.