One-liner: Split a problem across two separate AI sessions with different roles and contexts, then synthesize their outputs yourself — like managing a real team.
You'll need two AI chat windows open at the same time (two browser tabs, or two different AI tools — either works).
Pick a project or decision that has at least two distinct dimensions. For example: "Create a content strategy for launching our new product."
Chat A — The Strategist. Open your first chat and send:
You are a brand strategist with 15 years of experience. Your focus is positioning, audience targeting, and messaging clarity. You do NOT think about implementation details — that's someone else's job.
I'm working on: [your project]
Give me your strategic recommendations. Focus on: who the audience is, what the core message should be, and how to position this differently from competitors. Be specific and opinionated.
Chat B — The Executor. Open your second chat and send:
You are an operations-focused content producer. Your focus is practical execution: channels, formats, timelines, and resource requirements. You do NOT set strategy — you receive it and figure out how to make it real.
I'm working on: [your project]
Give me an execution plan. Focus on: which channels to prioritize, what content formats work best, a realistic timeline, and what resources I'll need. Be specific and practical.
Now you're the manager. Read both outputs. Notice what Chat A assumed that Chat B would question, and vice versa. Then write your own synthesis:
Optional bonus round: Take your synthesis and paste it back into one of the chats:
Here's the combined strategy and execution plan I've built from two different advisors. Poke holes in it. What's still weak?
Here's what you're about to do:
"Done" looks like: You have a plan that neither AI session could have produced alone, and you can articulate what each perspective contributed.
In AC-Basic-01, you simulated multiple perspectives in a single chat. Here, you're practicing a fundamentally different skill: managing separate agents with isolated contexts. This mirrors how real multi-agent systems work — each agent has a specific role, limited scope, and doesn't see the other's work. The human (you) acts as the orchestrator. This is the skill that scales: from two chats to entire AI-assisted workflows with specialized roles, handoff points, and quality gates.
Ready for more? Try AC-Advanced-01 — where you'll design a complete multi-agent workflow with defined roles, handoffs, and feedback loops.
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