Google Cloud Tech
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Building Production-Grade Multi-Agent Systems with ADK
Learn to build robust, state-aware multi-agent systems using Google's Agent Development Kit (ADK) and the Model Context Protocol (MCP) to handle orchestration, security, and persistence.
Google Cloud TechBuilding Full-Stack Apps with AI Sub-Agents
Google Antigravity uses voice-prompted sub-agents to orchestrate complex full-stack development, leveraging specialized guidance and MCP tools to build, test, and deploy multilingual applications.
Google Cloud TechOrchestrating AI Sub-Agents for Full-Stack Development
Google Antigravity uses voice-prompted sub-agents to automate complex full-stack builds, leveraging specialized guidance and recursive task orchestration to handle everything from backend logic to multilingual UI.
Building Scalable Multi-Agent Systems with A2A and Agent Registry
The Agent2Agent (A2A) protocol and Agent Registry solve agent sprawl by providing a standardized, discoverable way for AI agents to communicate, replacing hard-coded URLs with a centralized, governed directory.
Google Cloud TechBuilding and Scaling Data Agents with Google Cloud
Google Cloud is expanding its agentic AI ecosystem by providing persona-specific data agents, developer-facing APIs, and the new Data Agent Kit to streamline workflows across engineering, science, and analytics.
Google Cloud TechPowering Intelligent Agents with AI-Native Databases
Google Cloud is evolving databases into 'Agentic Data Clouds' by integrating AI primitives—like vector search, graph retrieval, and forecasting—directly into the SQL layer to provide agents with high-fidelity, secure, and real-time enterprise context.
Building AI-Native Search with Spanner
Google Cloud Spanner now integrates full-text, vector, and hybrid search directly into the database, eliminating the need for separate search engines, ETL pipelines, and data synchronization issues.
Building AI-Powered Search with Google Cloud Spanner
Google Cloud Spanner enables hybrid search by combining full-text, vector, and graph capabilities within a single, transactionally consistent database, eliminating the need for complex ETL pipelines and external search indexes.
Building and Scaling AI Agents with BigQuery and AgentOps
Google Cloud's Agent Development Kit (ADK) and managed MCP servers allow developers to build data-aware agents with minimal code, while integrated AgentOps provides real-time observability into agent performance and costs.
Scaling Enterprise AI: Agent Registry and ADK
Google Cloud's Agent Development Kit (ADK) and Agent Registry provide a governed, scalable architecture for orchestrating AI agents and tools, enabling enterprises to transform legacy APIs into secure, reusable MCP-compliant services.
Building Agentic Applications with Gemini 3.1
Google DeepMind and Cloud leaders discuss the evolution of Gemini 3.1, highlighting its multimodal reasoning, agentic capabilities, and the strategic importance of matching model size to specific enterprise use cases.
Building Agentic Systems with Gemini 3.1
Google DeepMind and Cloud leaders discuss the Gemini 3.1 model family, emphasizing its multimodal reasoning, agentic capabilities, and the importance of matching model size to specific enterprise use cases.
Building AI-Powered Apps: A Low-Code Guide for Small Teams
Small teams can modernize legacy applications by leveraging 'vibe coding' and managed database AI features like hybrid search and vector embeddings, allowing them to implement semantic capabilities without needing a team of AI experts.
Looker's Evolution: From Data Visualization to Data Agency
Looker is shifting from a passive BI tool to an active 'agentic' platform, using Gemini to enable conversational analytics, automated dashboard insights, and proactive, triggered workflows that turn data into direct action.
Implementing DeepMind's Deep Research API
Google's Deep Research API enables developers to integrate autonomous, multi-step research agents into their applications, automating complex information gathering, synthesis, and visualization tasks.
Scaling AI Agents and Inference on Google Cloud Run
Google Cloud Run is evolving from a web-service platform into a comprehensive runtime for AI agents, inference, and background tasks, introducing features like GPU support, sandboxed code execution, and custom scaling controls.
Scaling AI and Vibe Coding: What's New in Google Cloud Run
Google Cloud Run is evolving into a comprehensive platform for AI agents, 'vibe coding,' and high-scale microservices, introducing features like spend caps, GPU support, ephemeral sandboxes, and dedicated worker pools.
How the Model Context Protocol (MCP) Standardizes AI Integration
The Model Context Protocol (MCP) provides a standardized, open-source interface for AI models to discover and interact with external tools and data, replacing fragile, custom-built API integrations.
Google Cloud TechGoogle's Four-Layer AI Agent Stack: Architecture and Tools
Google's new agent stack provides a unified, scalable path from low-code UI to production-grade code, anchored by the Gemini 3.5 Flash model and the Agent2Agent (A2A) protocol.
Google Cloud TechIntegrating Gemini Intelligence into AlloyDB via AI Functions
AlloyDB AI functions allow developers to execute LLM-powered tasks like ranking, summarization, and forecasting directly within SQL, using optimized local models to achieve massive performance gains and cost reductions over standard row-by-row LLM calls.
Building Complex Software with Long-Running AI Agents
Long-running AI agents can execute multi-day, complex engineering pipelines—such as building an OS or optimizing 3D web scenes—by self-correcting through dependent tasks rather than relying on single-prompt generation.
Google Cloud TechGoverning AI Agents with Looker and MCP
By using the Model Context Protocol (MCP) to connect AI agents to Looker's semantic layer, developers can replace fragile raw SQL generation with governed, model-aware data interactions.
Architecting Long-Running AI Agents for Multi-Day Workflows
Move beyond stateless chatbots by implementing event-driven dormancy, durable checkpointing, and decoupled evaluation to manage complex, multi-day workflows.
Google Cloud TechManaging AI Agents in Enterprise Codebases
Transition from 'prompting' to 'coaching' by treating AI agents as digital interns, using custom skills, automated self-correction loops, and background task management to maintain production-ready standards.
Building AI Agents with Model Context Protocol (MCP)
The Model Context Protocol (MCP) acts as a universal adapter, allowing AI agents to securely interact with external tools and live data via a standardized input/output interface, decoupling agent logic from tool implementation.
Building Custom Vision Agents with Gemini, MCP, and Veo 3
Learn how to build a cloud-native vision agent that orchestrates real-time camera input, image style transfer via Nano Banana, and cinematic video generation using Veo 3, all controlled via natural language.
Building AI Agents with Google's Agent Development Kit (ADK)
A practical walkthrough on using Google's Agent Development Kit (ADK) to build autonomous agents that can interact with text-based environments, specifically demonstrated through a retro-inspired adventure game.
Google Cloud TechBuilding Long-Running, Event-Driven AI Agents with ADK
The Agent Development Kit (ADK) enables stateless, event-driven AI agents that maintain state across weeks of dormancy without token bloat, using a state-machine approach rather than traditional chat-based memory.
Google Cloud TechBuilding Multi-Agent Systems with ADK and A2A
The Agent Development Kit (ADK) and Agent2Agent (A2A) protocol enable specialized AI agents to collaborate on complex tasks, using an orchestration layer to resolve conflicts and incorporate human-in-the-loop decision-making.
Avoiding Cognitive Surrender in AI-Assisted Development
AI coding agents excel at speed, but they risk creating 'cognitive surrender' where developers lose the ability to maintain their own systems. To build reliable software, humans must remain the final authority, treating agents as tools that get you 70-80% of the way there, not as replacements for engineering judgment.
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