Stop trying to make one model do everything. Learn how to orchestrate specialized agents to solve complex problems together.
Just as you wouldn't hire one person to be CEO, Engineer, and Marketer, AI works better when roles are specialized.
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Assigning specific roles (e.g., "Python Coder", "Legal Analyst") creates narrower context windows and fewer hallucinations.
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Agents pass tasks to each other. A "Researcher" finds facts, passes them to a "Writer" to draft, who passes to an "Editor" to refine.
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A central agent that breaks down user requests and delegates sub-tasks to the correct worker agents.
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Agents can "chat" with each other to critique and improve work before showing it to the human.
See how a Manager Agent delegates a task to create a blog post about "SpaceX".
Follow the delegation process. The Manager receives a goal and breaks it down for the workers.
Role: Project Manager.
Goal: "Write a factual blog post about SpaceX's latest launch."
The Manager realizes this task needs facts first, then writing, then checking. It activates the Researcher first.
Role: Senior Data Analyst.
Tools: Web Search, Scraper.
It uses tools to find the latest dates, stats, and outcomes of the launch. It produces a bulleted list of facts and passes it back.
Role: Tech Content Writer.
Tools: None (Pure LLM).
It takes the Researcher's notes and turns them into an engaging narrative. It focuses on tone and flow, trusting the facts provided.
Role: Quality Assurance.
Tools: Formatting checker.
It reviews the draft. If it finds the tone too casual, it might ask the Writer to revise. Otherwise, it approves the final output.
One agent finishes, passes output to the next.
Best for: Clearly defined workflows like "Research -> Write -> Translate".
Test your Orchestration skills.
1. Why use multiple agents instead of one big prompt?
2. In a Hierarchical architecture, what is the role of the Manager?
3. Which pattern describes agents passing work in a straight line?