Google ADK Agents Workshop: Build, Evaluate, and Deploy Real Agent Systems
Build an end-to-end Google ADK agent with tools, state, routing, evaluation, debugging, failure handling, and deployment decisions using current official SDK behavior.
Google Agent Development Kit (ADK) for real-world execution
Build production-minded AI agents with Google Agent Development Kit by working through agent structure, tools, sessions/state, orchestration, evaluation, observability, deployment, and current ecosystem integrations.
The workshop follows current official ADK behavior and requires a functioning implementation with tools, state, tests, evaluation, failure handling, and a deployment plan—not a tour of example notebooks.
What you should be able to do when you finish
The point is not to collect AI vocabulary. It is to leave with a repeatable way to design, test, and improve a useful system.
Build a bounded agent using current Google ADK concepts and supported SDK patterns.
Connect tools, data, and external services with explicit action and permission boundaries.
Design state, sessions, routing, delegation, and multi-step behavior without creating uncontrolled autonomy.
Add evaluation, debugging, tracing, and failure handling before deployment.
Reason about deployment choices, model/provider options, security, cost, and operational ownership.
Complete and defend an end-to-end Google ADK agent implementation.
A practical curriculum without the filler
Each module moves from understanding to applied system design, testing, feedback, and stronger execution.
Current Google ADK architecture, agent types, and project setup
Start from current official ADK architecture and define a bounded job, agent structure, and deployment context.
Instructions, state, sessions, context, and structured behavior
Design instructions, state, sessions, context, and structured behavior so the agent remains understandable across turns and tasks.
Tools, integrations, actions, permissions, and MCP connections
Connect tools and integrations with explicit action boundaries, permissions, and current MCP/tooling patterns where useful.
Routing, delegation, multi-agent patterns, approvals, and recovery
Coordinate routing, delegation, approvals, retries, and recovery without creating uncontrolled multi-agent behavior.
Evaluation, debugging, observability, cost, and deployment
Use current evaluation, debugging, observability, performance, and deployment tooling to test whether the system is ready.
Build and ship a complete Google ADK agent system
Build and defend an end-to-end ADK agent system with a practical deployment and ownership plan.
A workshop where the build is the lesson
This page is built for people searching for focused, artifact-intensive practice. The experience centers on doing the work and leaving with a concrete system, workflow, or implementation artifact.
- Focused scope
- Hands-on build work
- Immediate feedback
- Revision in context
- A finished artifact or functioning system
Learn → build → test → improve
The course content matters, but the real product is learning how to turn AI into a system that survives contact with actual work.
Learn the system
Understand the pattern, boundaries, tradeoffs, and failure modes without drowning in abstract theory.
Build the design
Apply the idea to a realistic workflow or agent with explicit inputs, rules, tools, actions, and outcomes.
Test what fails
Use representative cases, verification, and failure analysis instead of treating one successful demo as proof.
Improve the system
Revise the design until it is clearer, safer, more reliable, more observable, and better suited to real use.
Built for people who want AI to become useful inside real work
This learning path works best for people who want to move beyond one-off demos and build Google ADK agent implementation with clear structure, controls, and outcomes.
Finish with decisions and artifacts you can reuse
AI Agentic School is designed around practical work. The exact outputs differ by product, but the goal is consistent: leave with stronger systems, clearer reasoning, and evidence of what you built and tested.
Learn → build → test → revise → apply.
Google ADK Agents Workshop FAQ
What will I learn in this google adk agents workshop?
You will build practical Google ADK agent implementation skills through structured lessons, system-level examples, applied exercises, testing, feedback, and concrete implementation decisions. The emphasis is on useful real-world execution rather than prompt tricks.
Is this google adk agents workshop suitable for beginners?
Yes, but it is action-oriented. The course begins with the core system concepts and then moves into increasingly realistic decisions, constraints, testing, and implementation thinking. Prior AI experience can help, but it is not required to understand the learning path.
Is this course tied to one AI vendor or tool?
No. AI Agentic School teaches durable system principles first. Current tools and vendor capabilities may be used as examples when useful, but vendor-specific behavior is treated as vendor-specific and can change over time.
How is AI Agentic School different from a prompt course?
Prompts are one component of an AI system. These courses focus on the wider architecture: inputs, context, state, rules, tools, actions, permissions, verification, testing, failure handling, observability, and improvement.
Can I use these skills for business or client work?
Yes. The learning is designed around repeatable system decisions that can be adapted to internal processes, client workflows, product features, research systems, content operations, automation, and other real-world use cases. The course does not guarantee a specific business outcome.
Keep building the system
Google ADK Agents Workshop: Build, Evaluate, and Deploy Real Agent Systems
Build the ADK agent, connect tools and state, test and evaluate the behavior, plan deployment, and finish with a system designed for real operation.