Multi-agent workflows without code

What is a multi-agent workflow

Think of it as a production line. Instead of one AI doing everything at once, you split the work across "agents" — each with a single job. One researches, another writes, another reviews.

You do not need any special tool. Just chain prompts together — the output of one becomes the input for the next. The payoff is not raw speed: it is control, because every step can be checked before the next one runs.

Example: write an article with 3 agents

Each agent gets a narrow brief and hands back something ready for the next stage:

Each agent does ONE thing. The final result is far better than a single prompt asking "write an article."

Orchestrator, specialists, and the handoff

Every workflow that works has an orchestrator: the one who takes the request, draws up the plan, and hands out the steps. The specialists each execute a slice — research, drafting, editing, analysis — and return a finished piece.

What decides whether the flow survives is the handoff: how context moves from one agent to the next. If Agent 2 receives only a summary, it rewrites on top of it and the pipeline loses whatever Agent 1 found.

In practice, the handoff is text: paste the previous agent's full output and add what changed. Once you move beyond copy and paste, open protocols like the Model Context Protocol exist precisely to connect agents to tools and sources of context.

Shared memory is the same idea at another level: a base brief — topic, audience, format, constraints — that you update once and reuse across every agent.

Other pipelines that work

• Project plan: Agent 1 lists risks → Agent 2 suggests actions → Agent 3 builds a timeline
• Presentation: Agent 1 extracts key points → Agent 2 creates slides → Agent 3 reviews tone and flow
• Decision: Agent 1 lists options → Agent 2 compares criteria → Agent 3 recommends with reasoning

It is the same logic as the 5 prompts that save hours, only organized into steps that feed each other.

The rule to keep it simple

Start with 2 agents. Only add a third when your 2-agent flow runs smoothly. The most common mistake is trying to build 5 agents at once and getting lost in the middle.

1. Write the base brief (topic, audience, format, constraints).
2. Run agent 1 and save the whole output, uncut.
3. Paste that output into agent 2, together with the base brief.
4. Review agent 2's output before you trigger agent 3.

Multi-agent workflows are the Ladder Rule taken to the next level: each rung becomes a specialized agent.

Frequently asked questions

Do I need more than one AI tool?

No. A single conversation with a solid history covers almost every pipeline. What changes is the discipline of keeping the roles separate and saving each step's output. A second tool only helps when it is genuinely stronger at one particular stage.

Do the agents remember what happened earlier?

Inside one conversation, the history plays the role of memory. Open a new chat and the memory is whatever you paste into it — which is why an up-to-date base brief beats rebuilding context from scratch every time.

How long does the first workflow take to set up?

The 3-agent pipeline in the example takes minutes to test. The bottleneck is not building it: it is checking agent 1's output before moving on. An error left uncorrected in the first step only grows in the second.

How is this different from a single prompt?

A single prompt depends on one round of quality; a workflow splits the work and lets you fix things step by step. You gain control and consistency, not necessarily speed.

If your flow is still one command, go back to the basics first: see beginner to pro and add the second agent once the first one comes back without rework.