Claude Code & Agent Teams: What Roles Actually Work for Vibecoding in the Cloud?
I just finished my first cloud project using Claude Code and I ran five different agents:
a PM
a QA
a Designer
an Engineer
a vp Engineer
It took two months to get the roles right-figuring out what each agent should and shouldn't handle, and making sure they coordinate smoothly.
My lesson: Agentic teams only work when each role is laser-focused and boundaries are clear.
Is anyone else using Claude Code (or similar tools) to build with multiple agents in a vibecoding workflow?
- What agent roles or setups have been most (or least) effective?
- How did you decide which tasks were "agent-perfect" and which needed your hands-on guidance?
- Any pain points or surprises while scaling up?
Would love to swap stories, wins, and fails as we figure out the boundaries of agentic coding together!
Replies
findable.
It's not really a Claude Code subagent (yet) but I've started to use Claude Code more and more also for technical SEO.
But for some technical SEO tasks it helps when the agent doesn't have to do all the steps (would take too long) and instead calls out to something more efficient like a command line tool or MCP.
That's why I eventually built an MCP around the SEO use case (https://www.producthunt.com/products/technical-seo-mcp).
I think into your list would also fit an agent specific for deployment and one for log analysis.
That said: do you see an advantage from having these specific agent definitions vs just prompting Claude Code directly?
One of the main advantages of subagents and the "task" concept in Claude Code is that you can save a lot of tokens in your main token window when you can pass a task to a subagent. In some cases Claude Code will even run them in parallel.
That was an unexpected advantage of the agent concept for me.
Love this breakdown. I noticed the "Designer" role struggled unless paired closely with Engineer. My win was letting Designer brainstorm options, then Engineer refine. Without boundaries, they just spun ideas endlessly without shipping.
Interesting setup! I leaned on a Researcher agent instead of VP Engineer, which actually saved time. Researcher gathered context, then Engineer built. My biggest pain was over-trusting VP-style oversight, ended up micromanaging anyway.
For me, the Designer role felt weakest. Still felt like I had to step in for anything creative or UI heavy.
I'd love to know more about how you decided when to let an agent own something vs. when you had to jump in yourself.
I've worked on agents that provide text output and also voice agents. For voice agents I've found that models which are faster and talk to the point have helped me a lot Because I tried GPT5, and other Deep LLMs also but they respond with too long context and for this that was too much.