Google Agent Development Kit (ADK)
Build production-grade autonomous AI Agents using Google Cloud Vertex AI & Gemini 1.5/2.0. Master multi-agent orchestration, ReAct decision loops, tool calling, and long-term memory state.
1. Google ADK Core Concepts
LLM vs Autonomous AI Agent
Standard LLMs only respond to input prompts. An Autonomous Agent equipped with Google ADK actively reasons, selects tools (BigQuery, PySpark, APIs), observes feedback, and loops until the goal is achieved.
Function Calling & Tools
Tools bridge Gemini LLMs to real-world systems. ADK allows agents to inspect databases, trigger Cloud Composer DAGs, execute Python scripts, and fetch real-time APIs safely.
Long-Term Memory & Context
ADK Memory Store uses GCP Firestore or Vector Search (Embedding API) to maintain user preferences, past session context, and enterprise schema across multi-turn interactions.
2. Multi-Agent Orchestration Patterns
Supervisor & Worker Agents
A Supervisor Orchestrator Agent receives the complex user goal and delegates sub-tasks to specialized domain agents (e.g. Data Engineer Agent, Data Science Agent, Reporting Agent).
Sequential Pipeline Agents
Tasks pass sequentially down an agent pipeline. Agent 1 cleans raw logs $\rightarrow$ Agent 2 generates PySpark transformations $\rightarrow$ Agent 3 runs validation checks before loading to BigQuery.
Peer-to-Peer Agent Swarm
Autonomous agents communicate collaboratively over a shared state channel, debating data insights and voting on optimal execution paths.
3. Interactive ReAct (Reason + Act) Loop Simulator
Step-by-Step ReAct Execution Trace
4. Interactive ADK Tool & Function Declaration Builder
# Generated Google ADK / Vertex AI Function Declaration...
5. Interactive Google ADK CLI Shell
Type help to list commands (agent run, tools list, memory status, trace, clear).