Hundreds of developers have already completed our first DEV Education Track, and today we're excited to keep the momentum going with our second tra...
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Good luck with this everyone!
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.
I'm totally going to participate in this one 😁
Awesome!
Can't wait to see what everyone builds with this education track!
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!
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
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?
The "specialized agents vs. monolithic prompt" framing is exactly right, and I think the ADK track structure will make this concrete in a way that's hard to get from documentation alone.
One thing worth flagging for people who go through this: the hardest part usually isn't building the individual agents, it's designing the orchestration contract between them. When Agent A hands off to Agent B, what does a "failed" output look like vs. a "successful but uncertain" one? Most teams I've seen skip this and end up with silent failures propagating through the pipeline.
A few patterns that help in practice:
Excited to see the A2A protocol approach. Curious whether it handles retries at the protocol level or leaves that to the orchestrator.
This is incredibly timely. I'm currently running 7 AI agents as my actual business team — engineering, finance, marketing, sales, strategic research, health, and a chief of staff that orchestrates them. Total cost: $200/month on Claude Max, running on a Mac Mini M4 Pro.
The multi-agent coordination problem is real and messy. Some things I've learned:
Trust scoring is essential. I built a composite algorithm (reliability 40%, speed 20%, goal completion 20%, efficiency 10%, activity 10%) scoring each agent 0-100. Engineering sits at 85. Marketing at 58. You need to know who to trust with what.
Fallback systems need as much engineering as your primary. Hit my Claude Max session limit, local LLM fallback wasn't calibrated — agents fabricated entire projects and invented fake agent names. 40 hours of chaos.
Agents will lie about task completion. My engineering agent marked a task "done" without doing the work. Caught it 3 days later. Now every completion requires a verification artifact.
The gap between "demo multi-agent system" and "production multi-agent system running real businesses" is enormous. Looking forward to this track.
This is exciting! I've been running a multi-agent setup on a Mac Mini for the past week — using OpenClaw + Claude as the orchestrator with sub-agents for different tasks (content publishing, code deployment, monitoring).
The biggest lesson: agent memory and state management is the real challenge, not the LLM calls. My agents write daily logs, share a data bus between cron cycles, and auto-heal when things break.
Curious about ADK's approach to inter-agent communication. Does it handle persistent state between runs, or is each agent invocation stateless?
Looking forward to this track!