---
title: Do You Need Several AI Agents for Workflow Automation?
description: Learn why one AI with good context is the better start for small-business workflows, what multi-agent setups cost, and when a second agent earns its place.
image: https://ai-marketinglabs.com/hubfs/several-agents-B.jpg
---

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# Do You Need Several AI Agents for Workflow Automation?

![Kelly Kranz](https://ai-marketinglabs.com/hubfs/1c8a298050a84a029fa51a6945685d90.png)

Kelly Kranz

Oct 12, 2026

[Twitter](https://twitter.com/intent/tweet?url=https://ai-marketinglabs.com/lab-experiments/do-you-need-several-ai-agents-for-workflow-automation&text=Do%20You%20Need%20Several%20AI%20Agents%20for%20Workflow%20Automation) [LinkedIn](https://www.linkedin.com/sharing/share-offsite/?url=https://ai-marketinglabs.com/lab-experiments/do-you-need-several-ai-agents-for-workflow-automation)

![Do you need several AI Agents for workflow automation?](https://ai-marketinglabs.com/hubfs/several-agents-B.jpg)

Probably not, at least not to start. Start with one AI that has good context: your policies, your price list, examples of past work, and clear instructions. Add a second agent only when a step needs different tools, different permissions, or can genuinely run in parallel. Several agents passing work to each other cost more and fail in more places.

**TL;DR**

- One AI with good context handles more than you'd expect.
- Multi-agent setups use far more tokens and add new ways to fail.
- Add an agent only for a step that needs different tools, access, or parallel work.

## Why do so many guides push multi-agent setups?

Because they're impressive to demo. A "research agent" hands off to a "writing agent," which hands off to a "review agent," and it looks like a small team working on its own. Search for AI agents for workflow automation and you'll find page after page of agent builders and lists of agents, many of them assuming more is better.

For a small business, the question isn't how many agents you can run. It's what the simplest setup is that does the job reliably.

## What does one AI with good context look like?

It's a single AI step (or a single assistant) with everything it needs to do the job well:

- **Instructions** written like a job description: what it does, what it never does, when to ask for help.
- **Reference material**: your price list, policies, service areas, product specs.
- **Examples**: a handful of past emails, quotes, or reports that show what good looks like.
- **Tools**: access to the one or two systems it needs, like reading the inbox and writing to the CRM.

Rick Kranz, CEO of AI Marketing Labs, prefers this approach for exactly that reason: "So it's not bots talking to each other because there's only one AI handling everything." When something goes wrong, there's one place to look. When you want to change how it works, there's one set of instructions to edit.

A lot of what gets sold as a multi-agent job is really one AI with better context. If the output is weak, try better instructions and examples before you add another agent.

## What do several agents actually cost?

More than you might expect. Anthropic, which built a multi-agent research system for its own product, reported that "agents typically use about 4× more tokens than chat interactions, and multi-agent systems use about 15× more tokens" ([Anthropic](https://www.anthropic.com/engineering/multi-agent-research-system)). Tokens are what you pay for. A setup that uses fifteen times as many to do a task has to be worth fifteen times as much.

The same write-up is clear about where multi-agent setups struggle: "domains that require all agents to share the same context or involve many dependencies between agents are not a good fit for multi-agent systems." A lot of small-business workflows look like that. The quote depends on the intake, and the follow-up depends on the quote. Everything shares context.

Researchers studying open-source multi-agent frameworks found the same kinds of problems. In "Why Do Multi-Agent LLM Systems Fail?", Cemri and colleagues cataloged 14 distinct failure modes across seven frameworks ([arXiv](https://arxiv.org/html/2503.13657v3)). Every handoff is another chance for something to get lost.

And the stakes aren't small. Gartner predicts "over 40% of agentic AI projects will be canceled by the end of 2027, due to escalating costs, unclear business value or inadequate risk controls" ([Gartner](https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027)). Cost and unclear value are the two risks an overbuilt agent setup adds.

## When is a second agent worth it?

Add one when a step genuinely needs something the first AI shouldn't have or can't do. Four good reasons:

1. **Different permissions.** The AI that reads customer emails shouldn't also have access to your bank account or payroll. A separate agent with narrow access keeps the risk contained.
2. **Different tools.** One step needs web research, another needs to write to your accounting system. Splitting them can keep each one simpler.
3. **Parallel work.** If you need to research twenty prospects at once, several copies of the same agent working side by side can be faster. This is the case Anthropic's research system was built for.
4. **A check on the work.** A second model that only reviews the first one's output, without taking actions of its own, can catch mistakes before they reach a customer.

Notice what's missing from the list: "it seemed more advanced." If none of these four apply, a better-equipped single AI is the answer.

## How do you tell which one you need?

Run through these questions for the workflow you have in mind:

- Does every step need to know what the other steps know? If yes, keep it as one AI.
- Would it be risky for one AI to have access to everything this workflow touches? If yes, split by permission.
- Is the work naturally parallel, like the same task on many items? If yes, run copies side by side.
- Is the single-AI version failing because it lacks context, or because the job is really two jobs? Fix context first.

Sometimes the real bottleneck isn't the AI at all. It's the messy input coming in, and one AI reading step fixes it. That's [why AI workflow automation finally works on messy inputs](https://ai-marketinglabs.com/lab-experiments/ai-workflow-automation-finally-works-on-messy-inputs).

## Where should you start?

Build the one-AI version first, on one workflow, with good instructions and examples. Run it for a few weeks with a person approving its output. Then look at where it struggles. If the struggle is missing information, add context. If it's a step that needs different access or runs in parallel, that's your case for a second agent.

Keep a simple log while you run it: what the AI got wrong, and why. After a few weeks, that log tells you whether you have a context problem or a second job.

Before you add anything, [estimate what the workflow is worth](https://ai-marketinglabs.com/lab-experiments/how-to-estimate-ai-roi-before-you-automate-anything), so you know how much complexity it can pay for.

It's how we approach [Custom AI Automations](https://ai-marketinglabs.com/ai-automation-services): the simplest setup that does the job, built on your tools and accounts, and handed over for your team to run.

## Frequently asked questions

What's the difference between an AI agent and an AI workflow?

A workflow follows steps you define. An agent decides some of its own steps toward a goal. Plenty of useful business systems are workflows with one AI step, not agents at all.

Can one AI handle a whole workflow?

It can handle the reading and writing parts of many workflows, with plain rules handling the routing. Give it clear instructions, reference material, and a few examples before you conclude it needs help.

Is it safe to give an AI agent access to my systems?

Give it the least access that does the job. Read-only access where it only needs to look, and a person approving anything that sends, pays, or deletes.

How much do AI agents cost to run?

You pay for the tokens they use, plus whatever platform they run on. Agents use more tokens than a single AI step, and multi-agent setups use far more, so check your provider's usage dashboard weekly at first.

 

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![Kelly Kranz](https://ai-marketinglabs.com/hubfs/1c8a298050a84a029fa51a6945685d90.png)

Written by

Kelly Kranz

With over 15 years of marketing experience, Kelly is an AI Marketing Strategist and Fractional CMO focused on results. She is renowned for building data-driven marketing systems that simplify workloads and drive growth. Her award-winning expertise in marketing automation once generated $2.1 million in additional revenue for a client in under a year. Kelly writes to help businesses work smarter and build for a sustainable future.

Keep *reading.*

[All posts →](https://ai-marketinglabs.com/lab-experiments)

[![Do You Need Several AI Agents for Workflow Automation?](https://ai-marketinglabs.com/hubfs/several-agents-B.jpg) Do You Need Several AI Agents for Workflow Automation? Probably not, at least not to start. Start with one AI that has good context: your policies, your price list, examples of past work, and clear instructions. ... Oct 12, 2026](https://ai-marketinglabs.com/lab-experiments/do-you-need-several-ai-agents-for-workflow-automation) [![How to Set Up AI Email Automation Without Paying for Every Email](https://ai-marketinglabs.com/hubfs/How%20to%20Set%20Up%20AI%20Email%20Automation%20Without%20Paying%20for%20Every%20Email.jpg) AI Systems How to Set Up AI Email Automation Without Paying for Every Email Set up AI email automation in two steps: a cheap sorting step that reads every email and tags it, then a more capable AI model that only sees the emails that ... Oct 12, 2026](https://ai-marketinglabs.com/lab-experiments/how-to-set-up-ai-email-automation-without-paying-for-every-email) [![AI Workflow Automation Tools vs a Custom Build](https://ai-marketinglabs.com/hubfs/AI%20Workflow%20Automation%20Tools%20vs%20a%20Custom%20Build.jpg) AI Workflow Automation Tools vs a Custom Build Off-the-shelf AI workflow automation tools are the fast start when your process is common and your data lives in popular apps. A custom build earns its cost ... Oct 12, 2026](https://ai-marketinglabs.com/lab-experiments/ai-workflow-automation-tools-vs-a-custom-build)

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