Extend Your AI Power: Build Custom AI Agents with Google AgentSpace

In our previous blog, The Future of Data Access: Google AgentSpace and Expanding Third-Party Connections, we explored how Google AgentSpace transforms data connectivity by integrating third-party platforms like Jira, SharePoint, and Workday, while preserving access control and making enterprise data searchable through AI. But connectors and actions only scratch the surface. The real power of Google AgentSpace lies in Custom AI Agents: intelligent, modular components that orchestrate workflows, respond to complex prompts, and make decisions in real time.

In this guide, we’ll show you how to build, customize, and integrate your own Custom AI Agents within Google AgentSpace. Whether you’re a developer or an AI product lead, you’ll learn how to extend your organization’s AI capabilities with a practical blueprint without starting from scratch.

Let’s dive in!

Why Custom AI Agents Matter in Enterprise AI Integration

While connectors bring your data and actions enable task execution, Custom AI Agents are what make your system intelligent. These agents allow you to embed reasoning, context, and decision-making directly into workflows, giving employees natural language access to enterprise systems.

By building your agents, you tailor AI to your business logic, whether it’s processing HR requests, automating DevOps tasks, or answering customer questions from internal data.

Key Benefits of Building Custom AI Agents

• Personalized Workflows: Create processes uniquely suited to your business.

• Contextual Awareness: Agents act on up-to-date, relevant data.

• Enhanced Efficiency: Automate complex tasks beyond prebuilt connectors.

• Future-Proof Architecture: Agents adapt to evolving data, platforms, and goals.

  
  

Core Pillars of Agent Development Kit (ADK): Build, Interact, Evaluate, Deploy

Google’s Agent Development Kit (ADK) streamlines the entire lifecycle of Custom AI Agents:

• Multi-Agent by Design: Create hierarchical, modular agent systems.

• Rich Model Ecosystem: Use Gemini, Gemma, or other foundation models from Vertex AI Model Garden or LiteLLM-supported providers like Anthropic, Meta, or Mistral AI.

• Flexible Tools: Equip agents with pre-built tools, Model Context Protocol (MCP) tools, or integrations like LangChain and LlamaIndex.

• Built-in Streaming: Enable multimodal, real-time conversations with bidirectional audio and video.

• Flexible Orchestration: Choose predictable workflows (Sequential, Parallel, Loop agents) or dynamic LLM-based routing (LLM Agent transfer).

• Integrated Developer Experience: Build locally with CLI/Web UI, debug with event/state inspection.

• Evaluation & Deployment: Evaluate agent performance and deploy anywhere, from Vertex AI Agent Engine to Cloud Run.

  
  

The AgentSpace Ecosystem: Where Your Agents Thrive

Before you build, understand how Custom AI Agents fit into the Google AgentSpace environment:

• Orchestrator: Directs prompts to the right agent or system.

• Connectors: Pull data from third-party platforms.

• Actions: Execute tasks via natural language.

• Agents: Interpret, reason, and respond intelligently.

  
  

  

  
  

Why Use Google AgentSpace for Custom AI Agents?

• Enterprise-Ready Security: Follows data residency and strict role-based controls.

• Unified Access: Agents integrate seamlessly with existing connectors and actions.

• Interoperability: Use protocols like the Agent2Agent (A2A) standard for multi-agent communication across platforms.

  
  

Selecting the Right Model: Powering Your Custom AI Agents

Every Custom AI Agent needs a powerful “brain.” Google Cloud gives you options:

• Gemini models: Advanced multimodal models (e.g., Gemini 2.5 Pro) with state-of-the-art reasoning and long-context support.

• Gemma models: Lightweight, fine-tunable models from open Gemini research—perfect for efficient or highly customized tasks.

• LiteLLM Integration: Expand model choices with providers like Anthropic, AI21 Labs, or Mistral AI.

Tip: Match your agent’s complexity and resource needs to the right model. Use Gemini for deep reasoning, Gemma for speed or local deployment, and third-party models for specialized tasks.

Building Robust Custom AI Workflows

A great agent does more than respond it drives entire workflows. With Custom AI Agents, you can:

• Chain Tasks: Set up sequential agents for multi-step processes.

• Parallel Execution: Use parallel agents for efficiency (e.g., processing HR and IT requests simultaneously).

• Dynamic Routing: Empower agents to delegate tasks using LLM-driven transfer logic.

  
  

Example Workflow: Onboarding Agent

1. Gathers user info.

2. Creates accounts in IT systems.

3. Triggers HR documentation.

4. Schedules orientation meetings.

   
   

Each step is handled by specialized agents, showcasing true AI agent orchestration.

Enabling Multi-Agent Collaboration and Interoperability

If your system uses multiple agents across ADK, CrewAI, LangGraph, or others, Agent2Agent (A2A) protocol standardizes communication. Backed by Google and 50+ partners, A2A prevents vendor lock-in by letting agents:

• Discover each other’s capabilities.

• Negotiate workflows.

• Coordinate tasks across different frameworks securely.

If you’re working purely within ADK, you can skip A2A by using built-in methods like shared session states or the transfer_to_agent API.

Deploying Your Agents: From Development to Production

Custom AI Agents need a secure, scalable runtime:

• Agent Engine (Vertex AI): Fully managed, optimized runtime for agents handles infrastructure, memory, and session state.

• Cloud Run: Deploy containerized agents with auto-scaling and integrated security.

• Google Kubernetes Engine (GKE): Best for complex, multi-container deployments needing fine-grained control.

  
  

Whichever runtime you choose, ensure:

• Proper authentication (OAuth 2.0, IAM service accounts).

• Secure endpoint registration via AgentSpace manifest.

• Monitoring with integrated analytics for continuous improvement.

  
  

Real-World Use Cases for Custom AI Agents

1. Financial Services Agent

• Processes loan applications.

• Flag compliance risks.

• Updates records securely.

2. Retail Inventory Manager

• Tracks stock across warehouses.

• Predicts out-of-stock items.

• Places automated restocking orders.

  
  

3. Healthcare Assistant

• Summarizes patient records.

• Schedules appointments.

• Monitors prescription refills.

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These examples show how Custom AI Agents improve speed, accuracy, and user experience while maintaining security and compliance.

API Connectivity: Flexible Options for Custom Applications

Google AgentSpace supports connecting agents to Dialogflow for hybrid generative-deterministic conversations. Use API integrations to connect agents with any system, enabling advanced workflows like:

• Fraud dispute resolution in banking.

• Automated HR case management.

• Intelligent CRM follow-ups.

  
  

With open A2A support, you can integrate agents built on LangChain, Agent Development Kit, or third-party frameworks, ensuring full enterprise AI integration without sacrificing flexibility or control.

Measuring and Improving Agent Performance

AgentSpace includes powerful analytics in the Google Cloud Console:

• Search analytics: Understand query patterns and satisfaction rates.

• Real-time feedback: Let users rate responses for instant quality signals.

• Deep insights: Analyze by query type, user group, or data source.

Use these insights to:

• Identify underperforming workflows.

• Optimize boosting/burying rules for search relevance.

• Refine tools or prompts used by your Custom AI Agents.

  
  

Final Thoughts: Build Intelligent, Adaptive Enterprises with Custom AI Agents

Building Custom AI Agents in Google AgentSpace isn’t just a technical exercise - it’s about transforming how your teams interact with enterprise systems. These agents automate repetitive tasks, provide context-aware assistance, and let your employees focus on innovation.

With AgentSpace’s modular architecture, robust Agent Development Kit, and open interoperability through the Agent2Agent protocol, your organization can deploy intelligent agents tailored to any business challenge.

So, what will your first Custom AI Agent do? Start building today and bring intelligence to the edge of your enterprise.

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