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GenAI Learn/Multi-Agent Systems & Execution
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Multi-Agent Systems & Collaboration

6 min read

You'll learn to

  • -Understand common multi-agent collaboration patterns
  • -Reason about when multiple agents beat a single agent
  • -Trace a shared-workspace collaboration including a revision loop

Writing a genuinely good blog post takes four different skills: research, writing, editing, and SEO optimization. One general-purpose agent asked to do all four tends to do all four adequately and none of them well, the same reason a single generalist employee rarely outperforms a small specialized team. Multi-agent systems apply that same intuition: give each skill its own specialized agent, and have them collaborate.

Communication Through a Shared Workspace, Not Direct Calls

The natural first instinct, having agents call each other directly, gets complicated fast: who calls whom, in what order, and what happens when one is waiting on another. The cleaner pattern is a shared workspace. The Researcher writes findings to it. The Writer reads those findings and writes a draft to the same workspace. The Editor reads the draft and writes feedback. No agent ever calls another agent directly. Every agent just reads and writes to one common place, coordinated by an orchestrator that decides what happens next.

Watch the workspace accumulate real artifacts below, including the part that makes this genuinely multi-agent rather than just four large language model calls in a row: the Editor sending work back for revision.

Four Specialists, One Shared Workspace

No agent calls another directly. Each reads and writes to the same shared workspace, including the Editor sending work back for revision.

Researcher

Gathering facts on "why remote teams struggle with async communication."

→ workspace: Research notes: 3 studies cited, 5 key stats on meeting overload.

step 0 / 5

The Editor Is Not a Rubber Stamp

A pipeline where every agent always approves the previous agent's work on the first try is not really collaboration, it is just four sequential generations. The revision loop above, where the Editor rejects draft v1 with specific feedback and the Writer produces draft v2 in response, is the part that actually earns the word "collaboration." An orchestrator with dependency tracking has to support this: Writer depends on Researcher's output, Editor depends on Writer's draft, and critically, Writer can depend on Editor's feedback too, looping back rather than moving strictly forward.

When Multiple Agents Actually Beat One

  • -Genuinely distinct skills that do not share much context or reasoning style. Research and SEO optimization draw on very different knowledge and produce very different kinds of output.
  • -A natural review or gate point in the workflow, where a second, differently-focused pass catches something the first pass would miss, exactly what the Editor role provides here.
  • -Work that can genuinely proceed in parallel once dependencies allow it, not just work that is conceptually separable but must still happen strictly in sequence anyway.

Multi-agent systems are not automatically better than a single well-prompted agent. They add real coordination overhead, more tokens spent, more places for something to go wrong, more latency from the back-and-forth. Reach for multiple agents when the task genuinely decomposes into distinct specialties with a clear handoff, not by default just because "multi-agent" sounds more sophisticated.

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