Google’s Agent Development Kit: Architecture, Tools, Workflow Engine and Real Uses
As AI systems move from single turn prompts to multi step autonomous workflows, cloud providers are racing to build toolkits that support true agentic behavior. One of the most talked about launches in this space is the Google Agent Development Kit ,a framework designed to help developers build structured, reliable AI agents on top of Google’s infrastructure.
what the Google Agent Development Kit is
how an agent development kit works
how it compares to similar tools from AWS, Microsoft and Oracle
what the typical agent development kit architecture looks like
how developers use these kits in real workflows
what to expect from an agent development kit tutorial
What Is Google Agent Development Kit?
The Google Agent Development Kit referred to as the Google Agent Developer Kit or Google’s Agent Development Kit . It is a collection of tools, APIs and runtime components for building AI agents that can :
plan
reason
call tools
manage workflows
maintain context
execute multi-step tasks autonomously
In simple terms:
Google’s Agent Development Kit is Google’s framework for building and running agentic AI systems using Gemini models and Google Cloud services.
It gives developers a standardized way to define agents, connect tools, orchestrate workflows and deploy autonomous pipelines without having to hand craft everything from scratch.
Why Do We Need Agent Development Kits?
The rise of agentic AI created a clear problem :
LLM prompts alone don’t produce reliable systems.
Organizations need:
memory
tools
workflows
error handling
grounding
monitoring
consistent execution
This is exactly why agent development kits are emerging across every cloud provider:
Microsoft Agent Software Development Kit → Azure OpenAI + Functions + Logic Apps
Oracle Agent Development Kit → Oracle AI + database-integrated automation
Agent Development Kit AWS → Bedrock + Lambda + Step Functions
Google’s offering fits into this same ecosystem but with Google specific strengths like search, grounding and Gemini integration.
What Google’s Agent Development Kit Typically Provides
While implementation details evolve, Google’s kit generally includes :
1. Agent Definitions
A structured way to define :
agent roles
LLM models
tools
input/output schemas
reasoning modes
2. Tooling Layer
Support for :
API calls
Google Cloud services
code execution
structured tool definitions
3. Memory + Retrieval
Often includes :
embeddings
vector storage
hybrid RAG systems
session state management
4. Workflow Orchestration
Agents can be arranged into :
step-by-step pipelines
branching logic
DAG-style workflows
event-driven chains
5. Deployment & Execution
Agents can run on :
Cloud Functions
Cloud Run
Vertex AI Pipelines
6. Monitoring & Governance
Enterprise-grade :
logs
traces
performance metrics
error breakdowns
access control
In short: it’s not just an LLM wrapper.
It’s a full agentic runtime.
Agent Development Kit Architecture
A typical agent development kit architecture—including Google’s—follows this layered model:
+-------------------------------------------------------+| Agent Definition || (roles, behaviors, models, constraints, schemas) |+-------------------------------------------------------+| Tools || (API interfaces, code execution, services) |+-------------------------------------------------------+| Memory Layer || (vectors, state, RAG, short/long-term memory) |+-------------------------------------------------------+| Workflow Engine || (DAGs, branching logic, retries, fallbacks, events) |+-------------------------------------------------------+| Cloud Runtime & Ops || (deployment, monitoring, logging, observability, auth)|+-------------------------------------------------------+
This layered design is what turns a raw LLM into a reliable agentic system.
How Google’s Kit Compares to Other Cloud Providers
Each major vendor has released its own take on an agent development kit:
Microsoft Agent Software Development Kit
Strong in:
enterprise governance
Azure OpenAI integration
automation workflows
Oracle Agent Development Kit
Focused on:
database-heavy environments
enterprise ERP systems
embedded AI workflows
Agent Development Kit AWS
Built around:
Bedrock model flexibility
Lambda
Step Functions
event-driven orchestration
Google’s Agent Development Kit
Stands out for:
Gemini models
Search + grounding
Vertex AI pipelines
Cloud Functions simplicity
Every kit reflects its cloud provider’s ecosystem.
**Agent Development Kit Tutorial **
Here’s a streamlined example of what a typical agent development kit tutorial flow looks like:
Step 1 - Create an Agent
agent:
name: ResearchAgent
model: gemini-pro
tools: [google_search, extract]
memory: vector_store
Step 2 - Register Tools
tool:
name: google_search
endpoint: https://api.google.com/search
method: GET
Step 3 - Define Workflow
workflow:
steps:
- agent: ResearchAgent
- agent: SummaryAgent
- agent: ValidationAgent
Step 4 - Deploy
Deployment targets may include:
Vertex AI
Cloud Run
Functions
Step 5 - Monitor
Using:
Cloud Monitoring
Cloud Logging
Traces
dashboards
This lifecycle is becoming the industry standard for agentic systems.
Why Google’s Kit Matters for Agentic AI
Agentic AI requires more than powerful LLMs.
It requires an ecosystem:
tools
memory
multi step orchestration
reliability
observability
cloud native execution
It brings structure to agent development and helps developers build systems not just prompts. As agentic architectures gain adoption across enterprises, frameworks like Google’s will define how AI agents are built, deployed and governed.