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What are AI Agents?

AI agents are intelligent systems that can perceive their environment, make decisions, and take actions to achieve specific goals. Unlike traditional chatbots that simply respond to queries, agents can:
  • Plan and execute multi-step tasks autonomously
  • Use tools to interact with external systems and APIs
  • Maintain memory across conversations and sessions
  • Reason and adapt based on context and feedback
  • Collaborate with other agents in multi-agent systems
Agents on Google Cloud leverage Gemini models for reasoning, with built-in support for multimodal inputs (text, images, audio, video) and advanced capabilities like code execution and function calling.

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Agent Architecture

A typical AI agent consists of several key components:
1

LLM Core

The reasoning engine (e.g., Gemini 2.5 Flash, Gemini Pro) that processes inputs and makes decisions
2

Tools & Functions

External capabilities the agent can invoke, such as:
  • Google Search for real-time information
  • Code execution for data analysis
  • Custom functions for business logic
  • API integrations
3

Memory System

Persistent storage for:
  • Conversation history
  • User preferences
  • Long-term knowledge
  • Session state
4

Orchestration Layer

Manages the agent’s workflow, including:
  • Task planning and decomposition
  • Tool selection and execution
  • Response generation
  • Error handling

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Agent Design Patterns

Google Cloud supports several proven agent design patterns:

Guardrail Classifier

Agents with built-in safety layers that classify and filter potentially harmful inputs or outputs before processing

Semantic Router

Multi-agent systems with intent detection to route requests to specialized expert agents based on user queries

Function Calling

Agents that can invoke external functions and APIs, with streaming capabilities for real-time responses

Task Planner

Research and planning agents that generate multi-step plans, execute them, and reflect on results

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Agent Platforms on Google Cloud

Google Cloud offers multiple platforms for building and deploying agents:

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Vertex AI Agent Engine

Managed service for deploying production agents with:
  • Automatic scaling and infrastructure management
  • Built-in Memory Bank for persistent context
  • Support for ADK, LangGraph, and custom frameworks
  • Enterprise-grade security and compliance
  • Integrated monitoring and observability
Best for: Production deployments, enterprise applications, teams wanting managed infrastructure

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Agent Development Kit (ADK)

Open-source framework for building custom agents with:
  • Python and Java SDKs
  • Modular architecture for composing agents
  • Built-in tools (Google Search, code execution)
  • Local development and testing
  • Easy deployment to Agent Engine
Best for: Developers building custom agents, rapid prototyping, flexible workflows

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Gemini Data Analytics

Specialized agents for conversational analytics:
  • Natural language queries over BigQuery and Looker data
  • Automatic SQL generation
  • Chart and visualization creation
  • Multi-datasource support
Best for: Business intelligence, data exploration, analytics workflows

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Common Use Cases

1

Customer Support

Always-on agents that handle customer inquiries, access knowledge bases, and escalate to humans when neededExample: Hotel concierge agent that remembers guest preferences across visits
2

Data Analysis

Agents that query databases, generate insights, and create visualizations from natural language questionsExample: “Show me sales trends by region for Q4” → SQL query → chart
3

Task Automation

Multi-step workflow agents that plan, execute, and monitor complex processesExample: Research agent that searches, synthesizes findings, and generates reports
4

Personalized Assistants

Agents with long-term memory that adapt to individual user preferences and contextExample: Always-on memory agent that consolidates information like a human brain

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Getting Started

# Install ADK
pip install google-adk

# Create agent project
adk create my_agent --api_key=YOUR_API_KEY

# Deploy to Agent Engine
adk deploy agent_engine my_agent

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Architecture Examples

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Single Agent with Tools

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Multi-Agent System

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Key Capabilities

Multimodal Understanding

Process text, images, audio, video, and PDFs with Gemini’s native multimodal capabilities

Long Context Windows

Handle up to 2M tokens with Gemini 1.5 Pro for entire codebases or long documents

Real-time Information

Access current web data through Google Search grounding

Code Execution

Run Python code for calculations, data analysis, and complex reasoning

Function Calling

Integrate with external APIs and services through structured function calls

Streaming Responses

Provide real-time, progressive responses for better user experience

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Next Steps

Agent Engine

Learn about the managed agent deployment platform

ADK Framework

Build custom agents with the Agent Development Kit

Data Analytics

Create conversational analytics agents

Sample Agents

Explore pre-built agent examples
Agents with internet access or code execution capabilities should be deployed with appropriate security controls and monitoring. Always implement guardrails for production deployments.

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