Building AI agents on Google Cloud

Summary: Building AI Agents on Google Cloud

This video provides a comprehensive guide and analysis on building AI agents using Google Cloud technologies, focusing on two main runtimes: Cloud Run and Vert.x AI. It covers AI agent concepts, architectural patterns, development frameworks, deployment, and interoperability standards.

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Key Technological Concepts and Features

1. AI Agents Overview

• AI agents are advanced application architectures powered by large language models (LLMs).
• They feature short- and long-term memory, access to contextual data, and orchestration of multiple tasks or sub-agents.
• User interactions can be synchronous (e.g., chat) or asynchronous (e.g., research, code review).
• Human-in-the-loop capability allows user moderation and control.
• Agents use various tools to extend capabilities beyond LLM limits, including:
  • Basic tools for math and conversions
  • Data access (databases, product catalogs)
  • APIs (first-party or third-party)
  • Image generation APIs
  • Browser automation (Chromium-based)
  • Code execution in sandboxed environments

2. Architecture for AI Agents

• Agents handle multiple user requests with streamed responses.
• Core components: serving/orchestration, model reasoning, memory, data access, and tools.
• Runtime requirements: scalability, cost-effectiveness, low latency, reliability, developer-friendly experience, language flexibility, and streaming support.

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Building AI Agents on Google Cloud Run

• Cloud Run is ideal for hosting AI agents due to:
  • Automatic, on-demand, rapid scaling
  • Pay-per-use pricing (no flat fees or pre-provisioning)
  • Fully managed with zonal redundancy and enterprise-grade security/compliance
  • Supports any language/framework (Python, JavaScript, Go, etc.)
  • Built-in HTTPS endpoints with streaming (HTTP chunked transfer, HTTP2, WebSockets)
  • Seamless integration with Google’s Gemini API (no API keys needed)
• Implementation example:
  • Use Cloud Run to serve and orchestrate agents running frameworks like Langraph or Agent Development Kit (ADK).
  • Integrate Gemini models, fine-tuned models on Cloud Run with GPUs.
  • Use Firestore or Cloud SQL (with PG vector plugins) for retrieval augmented generation (RAG).
  • Host tools on Cloud Run or call external APIs.
• Demo by Vita (Engineer, Cloud Run Team):
  • Introduced Langraph framework for building agents.
  • Explained limitations of standalone LLMs (frozen knowledge, no external interaction).
  • Contrasted chains (fixed workflows, reliable but rigid) vs. agents (dynamic tool-calling loops, flexible but less reliable).
  • Presented a hybrid approach using graph/state-machine control flow via Langraph for reliable yet flexible agent behavior.
  • Showcased a customer support app with multiple AI agents handling case context, SOP reading/planning, and customer replies.
  • Demonstrated deployment workflow using gcloud run deploy:
    • Source upload to Google Cloud Storage
    • Cloud Build containerization (using Buildpacks if no Dockerfile)
    • Image pushed to Artifact Registry
    • Cloud Run revision created and traffic migrated automatically
  • Highlighted Cloud Run’s monitoring dashboard (request count, latency, container instances, CPU/memory usage).

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Building AI Agents on Vert.x AI

• Vert.x AI is a comprehensive Google Cloud platform supporting the full AI agent lifecycle:
  • Access to Gemini models, open-source models (Model Garden), and bring-your-own models.
  • Native integration with Google Cloud enterprise data sources and tools.
  • Pre-built connectors, custom API calls via Apigee, and workflow triggers.
• Agent Builder Components:
  • Agent Development Kit (ADK): Open-source framework for building sophisticated, reliable agents.
    • Offers a developer-friendly experience similar to traditional software development.
    • Features a built-in local web UI for testing/debugging agents.
    • Supports richer interactions: audio, video streaming with minimal code changes.
    • Model-agnostic and deployment-agnostic (can run on Cloud Run, GKE, on-premises, other clouds).
    • Supports interoperability protocols: Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocol.
    • Additional capabilities: built-in agent evaluation, tool generation from OpenAPI specs.
  • Agent Engine: Serverless runtime optimized for agents.
    • Handles scaling, security (VPC-SC, compliance), cloud monitoring, and agent evaluation.
    • Deploy containerized agents with minimal code.
  • Agent Garden: Repository of pre-built agent samples, modular tools, connectors, and reusable code snippets.
    • Helps reduce boilerplate and accelerates development.
• Agent-to-Agent (A2A) Protocol:
  • Open standard co-developed with 50+ industry partners.
  • Enables