Introducing Our Next DEV Education Track: "Build Multi-Agent Systems with ADK"

Hundreds of developers have already completed our first DEV Education Track, and today we're excited to keep the momentum going with our second track in partnership with the team at Google AI.

This intermediate-level track will guide you through building distributed multi-agent systems using Google's Agent Development Kit (ADK), Agent2Agent Protocol (A2A), and Cloud Run. You'll learn to architect AI applications as coordinated teams of specialized agents rather than relying on a single monolithic prompt.

How to Complete This Track

This DEV Education Track is a three-part experience: 1) an expert tutorial followed by 2) a hands-on build and 3) a writing assignment. Work through all three parts and you'll earn the exclusive "Multi-Agent Systems Builder" badge.

📖 Part 1: Follow the Expert Tutorial

Start with this comprehensive Codelab:

Building a Multi-Agent System  |  Google Codelabs

“Build a distributed multi-agent system from scratch using the Google Agent Development Kit (ADK) and A2A protocol.”

codelabs.developers.google.com

You'll learn:

• Why specialized agents are more effective than monolithic prompts
• The architecture of distributed multi-agent systems
• How to master orchestration patterns
• How to implement the Agent-to-Agent (A2A) protocol for distributed communication

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🤖 Part 2: Build Your Own Multi-Agent System

After you've worked through the tutorial, it's time to put your new skills to the test!

Your assignment is to build a multi-agent system that takes a task that would normally require "one giant prompt" and breaks it into specialized roles, accessible through a web interface.

Requirements:

• Multiple specialized agents: Each agent has a focused responsibility
• Deployed to Google Cloud Run: Agents must run as separate microservices
• Frontend application: Web interface deployed to Cloud Run that users interact with

We encourage you to come up with your own apps, but here are some ideas if you need inspiration:

• Email Drafter: Topic agent suggests what to write → Writer agent creates draft → Editor agent polishes tone
• Gift Idea Generator: Profile analyzer understands the recipient → Idea finder suggests options → Budget filter removes expensive items
• To-Do Prioritizer: Task analyzer reviews your list → Urgency checker ranks by deadline → Focus agent picks top 3 for today

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✏️ Part 3: Earn Community Recognition

Everyone who completes the track by sharing their assignments will earn the exclusive "Multi-Agent Systems Builder" badge on their DEV profile!

Your submission should include:

• What you built: Describe the problem your system solves
• Cloud Run Embed: Embed your web app directly into the submission
• Your agents: Explain each agent's specialized role and how they work together
• Key learnings: What surprised you? What was challenging?

Use our official submission template to share your assignment:

Share Your Project

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Badge Design 😍

Our badge acts as a certificate of completion that you can highlight on your DEV profile. It'll look like this:

Our team will review submissions on a rolling basis with badges awarded every few days. There's no deadline, so take your time and build something you're proud of!

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Why Multi-Agent Systems?

Multi-agent systems are one of the most important architectural patterns in production AI development. Just as you wouldn't ask a single developer to handle frontend, backend, database, and DevOps all at once, modern AI systems benefit from specialization. This track teaches you to create focused agents and coordinate them to solve complex problems that would otherwise overwhelm a single prompt.

We can't wait to see what you create. Happy building! ❤️

Comments

[Ben Halpern The DEV Team]

Good luck with this everyone!

[Ofri Peretz]

The shift from monolithic prompts to specialized agents is the right architectural direction, but one thing I'd love to see covered in the track is how you handle trust boundaries between agents. When Agent A passes output to Agent B as input, you've essentially created a prompt injection surface at every handoff point. Curious if the A2A protocol has any built-in sanitization for inter-agent messages or if that's left to the developer.

[Tombri Bowei]

I'm totally going to participate in this one 😁

[Jess Lee The DEV Team]

Awesome!

[Vivian Jair]

Can't wait to see what everyone builds with this education track!

[MaxxMini]

This is incredibly timely! I've been running a multi-agent system (OpenClaw-based) on a Mac Mini for autonomous content creation and distribution — sub-agents for coding, SEO, writing, and monitoring all coordinating via shared state files and cron jobs.

The biggest lesson: agent-to-agent communication design matters MORE than individual agent capability. Getting agents to validate each other's work was the hardest part. Excited to see Google's approach to this with ADK!

[Guilherme Zaia]

Multi-agent systems shine until you hit production inter-agent failures. ADK abstracts orchestration, but who debugs cascading timeouts between 5 Cloud Run instances at 3 AM? The real test isn't 'can it work'—it's 'can you trace why Agent C hallucinated because Agent A's output drifted'. Where's the observability story?

[Pavel Gajvoronski]

This is the exact problem we hit at scale. With 31 agents in production chains, "Agent C hallucinated because Agent A's output drifted" happened to us literally in week one.
Two things that helped: (1) every agent call gets a trace_id, and every chain step becomes a span linked to that trace — so you can follow the full path from request to failure in seconds. (2) We built a cryptographic blame-finding protocol (JEP) where every delegation and judgment is hash-linked — you can trace backwards from any failed output to the exact agent and decision that caused it.
The observability gap in multi-agent systems is so real that we ended up building a separate product for it — TraceHawk, MCP-native observability specifically for AI agents. Because you're right — standard APM tools (Datadog, Sentry) don't understand agent delegation chains.

[angel t. duran]

I wish I could get cloud shell but I don't like putting my mastercard in google services, else I could find out where to look

[Mykola Kondratiuk]

honestly the timing of this is perfect - I've been building multi-agent setups for a few months and the hardest part isn't the code it's figuring out how agents should hand off context to each other. ADK looked interesting when I first saw it but I wasn't sure it was production-ready. curious whether this track covers error handling in long-running chains - that's where I kept hitting walls

[Pavel Gajvoronski]

Great timing on this track — multi-agent orchestration is exactly what I've been building for the past 3 months.
I run 31 specialized agents in a 4-layer hierarchy (Supervisor → Business → Tool → Infrastructure) for Kepion, an AI company builder. A few things I learned the hard way that this track's participants might find useful:

1. Loop detection is non-negotiable. When Agent A delegates to Agent B and B delegates back to A, you get infinite loops that burn through your API budget in minutes. We added max depth (10), ping-pong detection, and a cost circuit breaker with 4 levels ($2/request, $10/agent/hour, $50/business/day, $100/platform/hour).
2. Model routing saves 80-85% on costs. Not every agent needs your most expensive model. We use 4 tiers — Free (Llama 3.3) for routing, Budget (DeepSeek) for content, Performance (MiniMax) for coding, Premium (Claude/Opus) for architecture. Auto-escalation on failure, auto-downgrade on consistent success.
3. Checkpoint & replay changes everything. 95% reliability per step × 20 steps = 36% end-to-end success. We checkpoint after every chain step and resume on failure instead of restarting. Dead letter queue for chains that fail after all retries. Excited to see what people build with ADK + A2A. The protocol-level interop between agents is where the industry is heading. If anyone's interested in the architecture details, I've been documenting the whole build on dev.to: Building Kepion series

[ANIRUDDHA ADAK]

Sounds exciting, count me in.

[Davincc77]

This is a great track. The shift from “one giant prompt” to specialized agents with clear responsibilities is exactly where production AI systems are heading.

One layer I’d love to see discussed more in multi-agent systems is portable state.

Once agents become distributed services, the hard part is not only A2A messaging. It is also:

• what context moves from one agent to another;
• what state survives between runs;
• what constraints travel with the task;
• what must be verified before the next agent acts;
• what gets logged when an agent hands work off.

In small demos, this can live in a prompt, shared JSON file, or markdown state file. But in production, handoff state starts to look like an artifact that needs structure, versioning, privacy rules, and sometimes encryption.

That is the direction I’m exploring with .klickd: a portable, encrypted context file for agents.

Not a replacement for ADK or A2A — more like a state layer that an agent system can load before work starts.

The current work is moving toward:

• media_profile for project-scoped creative/video workflows;
• verification_gates for “check before action” rules;
• human_veto_policy for sensitive actions;
• claim_sources for executed / inspected / assumed claims;
• verification_artifacts so agents do not re-run expensive tests just to rediscover failures.

The interesting question for me is:

If A2A defines how agents talk, what defines the portable memory and constraints they carry between tasks, tools, and sessions?

For context: github.com/Davincc77/klickdskill

Would be very interested to see how people in this track handle state, handoff, validation, and long-running workflow continuity.