Develop AI Agents with Google ADK | MCP | A2A - Masterclass

Test your agent with SDK run to deploy locally and interact via the command line, with bullet points and pipeline summaries aligning with the web interface.

Run your ai agent locally from your desktop using a gemini api key as the backend, with a PyCharm project and a local UI at 127.0.0.1:8000.

Authenticate an agent with a Google service account for Vertex AI, assign Vertex AI user permission, manage the JSON key, and adopt workload identity federation and Secret Manager.

Learn to use non-google models with ADK, authenticate with api keys from Anthropic or OpenAI, select cloud 37 sonnet latest, and connect via Vertex AI or self-hosted endpoints.

Explore the preview agent designer UI, create a root agent with instructions, generate code, add sub agents and tools like Google search and URL context, and test in the UI.

Explore deployment options for AI agents, including Vertex AI Agent Engine, Cloud Run, and GKE, and consider memory bank and project architecture needs.

Test the deployed agent with curl by creating a session and querying via the stream query url, then review api urls and runtime metrics in the agent engine console.

Test the agent from a Python program via REST URLs in Colab Enterprise. Create and use a session, handle authentication, and optionally enable streaming responses from the LLMs.

Explore Google's agent starter pack to deploy and create base, agent-to-agent, protocol supported, rag and live agents in minutes, guided by documentation and the Google Cloud Platform GitHub resources.

Test your agent locally using the make install and make playground commands, view the local agent on port 8501, and customize the app directory and agenda file for different agents.

Deploy a live agent to Cloud Run with make deploy, verify its URL and public access, and explore the agent starter pack with UI support for audio, video, and text.

Deploy an academic research agent from the agent garden using the agent starter pack in cloud shell, deploying to the cloud agent engine in 5 to 10 minutes.

Discover how tools transform a model into an agent by connecting external systems, enabling real-time data, advanced calculations, and multi-tool configurations with MCP and third-party tools.

Learn to use the BigQuery tool bundled with the SDK to query the employee data set. Configure authentication with application default credentials and set read-only access.

Explore the big query tool as an ai agent that maps tables via dataset ids, answers plain-English questions, and executes sql to reveal employees, managers, engineers, salaries, and leaves.

Explore multi-agent systems and how multiple agents coordinate to achieve a larger goal. Compare monolith and microservice-like architectures and preview ADK tools for your first multi-agent system.

Modularize your multi-agent system by separating subagents into independent, environment-specific components, enabling individual backends, cleaner imports, and easier testing and maintenance.

Explore workflow agents, special agents with predefined control flow that orchestrate subagents without relying on large language models, including sequential, loop, and parallel types for deterministic execution.

See how the loop agent coordinates sequential and parallel agents, including a code writer, reviewer, and refiner, to produce and polish a Python program via a web demo.

Explore input schema in action by validating a country input with Pydantic's base model, demonstrating JSON format data and how instructions enforce or relax schema to fetch a country's capital.

Set the ai agent's config by adding description and instruction, define generate content config with temperature 0.2 and max output token 400, and apply a safety filter.

Set up and configure a Vertex AI search data store to ground AI agent responses by indexing unstructured documents from cloud storage and other sources, enabling fast, accurate answers.