Multi-Agent Workflows

Team Up Your AI

What if your AI assistant could clone itself and have different experts work on different parts of your task simultaneously?

Teams

Create teams of specialist agents:

• Explorer — investigates the codebase
• Coder — implements changes
• Reviewer — checks for bugs and improvements

Execution Modes

• Sequential: Explorer → Coder → Reviewer (pipeline)
• Parallel: All agents work simultaneously

Discussion Mode

Agents can refine each other's work. The reviewer might suggest improvements that the coder implements in a second pass.

Why multiple agents matter

A single AI assistant can do a lot, but complicated work usually has different phases. Research is not the same as implementation. Implementation is not the same as review. Review is not the same as writing a final summary. When one agent tries to do everything, it can lose focus or rush through the parts that need a different mindset.

Multi-agent workflows split the work into roles. That makes the task easier to supervise because each agent has a clearer job. In WindOp, this is especially useful for desktop and coding workflows because the app can combine model reasoning, tools, files, browser work, and user checkpoints. The multi-agent docs cover the feature side, but this post is the practical mental model.

The goal is not to spawn agents for everything. The goal is to use them when the task is large enough that role separation improves the result.

Common agent roles

Here are the roles I reach for most often:

You do not always need all of these. For a small code fix, Explorer, Coder, Reviewer may be enough. For research-heavy work, Researcher and Summarizer may be enough. For risky desktop automation, Planner and Verifier matter a lot.

Sequential vs parallel execution

The simple rule:

Sequential is safer when the task has a critical path. If the coder needs the explorer's findings before editing, do not run both blindly at the same time. Let the explorer finish, then hand the findings to the coder.

Parallel is faster when the work is independent. For example, one agent can inspect docs, another can inspect pricing copy, and another can review blog SEO. They do not need to touch the same files, so you get useful results faster.

Hybrid is usually the best for serious work. You split the information gathering, then consolidate before editing. That avoids duplicate work and reduces conflicts.

Real-world examples

Example 1: Fix a broken Next.js build

A good multi-agent setup:

1. Explorer reads the build error and finds the likely files.
2. Coder makes the smallest fix.
3. Verifier runs npx next build.
4. Reviewer checks whether the fix caused a hidden regression.

Prompt:

Use a sequential workflow. First inspect the build failure and identify the root cause. Then make the smallest code change. After that, run the build and review the changed files for regressions.

This is better than one giant prompt because it prevents the model from editing before it understands the problem.

Example 2: Research and write a comparison page

A good setup:

• Researcher A studies competitor features.
• Researcher B studies pricing and packaging.
• Writer drafts the page.
• Reviewer checks claims and internal links.

This can run partly in parallel because competitor research and pricing research are separate. The writer should wait until the researchers finish.

Example 3: Desktop cleanup

A safe setup:

1. Explorer lists files and proposes categories.
2. Planner creates a move plan.
3. User approves.
4. Operator moves files.
5. Verifier checks the final folder structure.

For desktop automation, I like this because it puts the user in control before anything changes. If you want the basics of how WindOp controls the screen, read Desktop Automation 101.

Advanced patterns

The debate pattern

Use two agents to propose different approaches, then a reviewer chooses the safer one. This is useful when you are not sure whether to refactor, patch, or leave something alone.

Agent A: propose the smallest fix.
Agent B: propose the cleaner long-term fix.
Reviewer: compare both and recommend one with risks.

The red-team pattern

Have one agent intentionally look for failure cases. This is useful for security, payments, file deletion, automation, and anything that could affect user trust.

Review this plan as a skeptical reviewer. Find ways it could delete data, expose secrets, mislead the user, or fail silently.

The parallel draft pattern

For writing or design, ask two agents for independent drafts, then merge the strongest parts. This can improve creative work, but it needs a strong editor at the end.

The specialist model pattern

Different agents can use different model strengths. A cheaper model can gather notes. A coding model can patch files. A premium model can review. This connects directly to AI Models today for general use: model choice should follow the role.

When not to use multi-agent

Do not use multiple agents for tiny tasks. If you need to rename one file, summarize one paragraph, or change one button label, one agent is enough. Multi-agent workflows add coordination overhead. They are worth it when the work is large, risky, ambiguous, or parallelizable.

A quick rule:

How to start in WindOp

If you are new, install from the download page, then start with a small sequential workflow. For example:

Inspect this project, explain the likely SEO improvements, and wait for my approval before editing. After edits, run the build and summarize what changed.

That prompt gives you the benefits of role separation without making the workflow too complicated. Once you trust it, move into parallel work:

Have one agent review the blog content, one agent review metadata, and one agent review structured data. Bring the findings back before making changes.

That is where WindOp becomes more than an assistant. It becomes a small operating team on your PC, with you still making the final call. The future is not just asking AI for answers. It is coordinating AI workers that can research, build, test, and explain the result in one workflow.