Multi-agent collaboration means specialized AI agents divide work, share context, and coordinate toward a shared goal. Multi-agent collaboration is distinct from a multi-agent system, which is the broader setup in which agents may cooperate, compete, or pursue different goals.
A single AI agent can help your marketing team, but a group of them can really get things going. The practical question is whether agent orchestration improves the work enough to justify extra cost and oversight. This guide walks through a campaign workflow, the technology behind it, and how to test a small pilot.
Table of Contents
- What are multi-agent systems?
- How do multi-agent systems work?
- Multi-Agent Systems vs. Single AI Agents
- Benefits of Multi-Agent Systems
- Challenges of Multi-Agent Systems
- How to Implement Multi-Agent Systems
- Multi-Agent Systems in the Real World
- Frequently Asked Questions About Multi-Agent AI Systems
- Are you ready for multi-agent systems?
What are multi-agent systems?
A multi-agent system (MAS) is a network of AI agents that operate independently and collaborate to solve complex challenges. Each agent in a MAS manages a specific task or area but communicates with other agents to decide on actions and adapt as needed. A research agent might identify a customer objection, then pass the evidence to a content agent that revises a campaign brief.
A multi-agent LLM system uses large language models to power its agents. The agents can use the same underlying model but with different instructions, tools, and context. Each role in a multi-agent LLM architecture needs a defined contribution to the task.
For teams evaluating multi-agent collaboration, the practical work starts with choosing agents and deciding how to manage them. gives teams ready-made agents for marketing, sales, and customer service, and agent builder lets them create custom agents and workflows from their CRM data with no coding required.
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How do multi-agent systems work?
Multi-agent systems operate by assigning specialized tasks to agents that interact within a shared environment. You see this structure on your human marketing team now:
- A campaign strategist who researches the target audience and positioning.
- A copywriter who crafts content to reach those audiences.
- A visual designer who catches people’s attention with images and video.
Some teams have one person playing multiple roles (or, sometimes, all the roles). But on a larger team, each person operates autonomously to do their work while communicating within the shared framework of team goals and desired outcomes.
A multi-agent system runs similarly. Each agent manages its tasks but can negotiate, delegate, and learn from one another. Plus, these to ecosystem changes without human input.
To help contextualize a MAS, I spoke with , the former Chief Strategy and Finance Officer at . LeVine walked me through an explanation of how a system of can work together.
Shared Memory and Context
Shared memory gives collaborating agents access to approved context relevant to the task. For a marketing campaign, that could include the audience brief, product facts, budget limits, approved claims, and previous results. Each agent also needs the current task status to distinguish a draft from approved work. Then, the agent processes the information and prepares it for the MAS.
Message Passing and Handoffs
The next agent takes the campaign data and develops multiple campaign strategies and creative direction. In addition to what the human marketer provides, the agent can autonomously query other data sources and past campaign content to craft strong strategic angles. Simply put, the previous agent provides direction and supplies the information needed for the next agent to do it. This is where a handoff occurs.
Agents can exchange these messages through APIs, framework calls, or messaging queues. Queues hold messages until another component processes them. For agents on different platforms, the provides a standard for agent communication.
Role-Based Coordination
Give each agent a defined job and a clear completion rule. The agents may work in parallel on independent tasks, such as audience research and competitor research. Draft approval, however, must wait until the required checks are complete. The campaign workflow could use these roles for agents.
- Intake and strategy: Turn the brief and approved research into a campaign plan.
- Content: Draft assets that follow the plan and cite support for factual claims.
- Review and performance: Check the assets against the brief, propose an A/B test, and report actual results after launch.
Role definitions give teams a way to evaluate ready-made agents. For content planning, try 糖心原创’s , which can turn a simple goal into a multichannel campaign plan. For sales, deal agent can turn meetings, calls, emails, and pipeline signals into recommended next steps, while keeping deal records and deal plans current.
Orchestration and Human Review
Agent orchestration routes tasks, manages handoffs, and coordinates review among agents. An orchestrator can use predefined rules or an AI agent. A supervisor agent can assign work, assess returned results, and request another attempt within configured limits. Choose the that fits the task’s dependencies.
Human approval should be required before an AI agent takes actions that are difficult to reverse, expose sensitive information, or carry significant consequences. Set a retry limit and route unresolved disagreements to the campaign owner.
In a sales workflow, deal agent can turn a buyer interaction into a proposed next best action or email draft. Teams can choose which CRM property updates run automatically and which stay with a rep for review, so routine updates move quickly while consequential changes still reach the right person.
LeVine noted the value that humans bring to the development and deployment processes. “All of these stages would need human validation, especially early on,” he said. With human support, these MAS can align and optimize a campaign’s impact while reducing risks to your brand.
Multi-Agent Systems vs. Single AI Agents
Single AI Agents: Output
Single AI agents can work autonomously alongside teams, usually to support a very specific function or task. The name of that game is “output.” Teams can give those agents access to a data trove and broad functional authority, but the result is almost always an output. Generate a blog post, summarize a data report, draft an ad — teams get something at the end of the agentic process.
On marketing teams today, I think these agents operate as particularly well-educated interns. You wouldn’t leave them entirely on their own (yet), but you can trust they’ll do a fine job. Validate their work on actual tasks before granting more authority. Common agentic uses include:
- Frontline customer support
- Content creation
- Campaign optimization
- Data analysis
Multi-Agent Systems: Coordination
A collaborative MAS distributes work across agents with separate responsibilities. Each agent plays a role in achieving a directed outcome, and they communicate with each other to reach that goal. Done well, MAS should feel less like using a tool and more like managing a team.
AI agents are still finding their footing within most organizations — , albeit carefully. Multi-agent systems stretch a team’s technical capabilities even further than single agents do. Still, I’ve found some technical-forward marketers using MAS as a campaign manager or operating in a support capacity.
Note: A single AI agent can be less suitable when a workflow benefits from distinct specialties, parallel execution, or handoffs between specialist roles. On the other hand, sequential steps alone don’t justify extra agents. So compare both approaches on the same work before choosing.
Benefits of Multi-Agent Systems
With power and opportunity available to your team, a multi-agent system can bring impressive benefits. Let’s cover a few of the most crucial.
Cross-Functional Multi-Agent Collaboration
When I’ve assembled teams in the past, I’ve sought experts in particular fields. That could be a marketing team with a range of skills, such as copywriting, long-form writing, and visual design.
A collaborative system applies a similar division of responsibilities. An agent might focus on strategy, content, or testing. While each agent operates within its prescribed function, it provides valuable data to its fellow agents in service of the team’s goal. That exchange can help agents use findings that would otherwise remain inside separate tasks.
The advantage is task-specific. In a , Google researchers found an 80.9% performance improvement over a single agent, with centralized coordination, on parallelizable financial reasoning tasks. However, every multi-agent variant tested performed worse on sequential PlanCraft tasks, with declines of 39%–70%. From there, a practical rule emerges: split work when the parts can make useful progress independently, and test whether handoffs help.
Learning and Adjusting On-the-Go
Within a MAS, agents can share knowledge and imitate effective behaviors, thereby learning and adjusting over time. LeVine noted that this feature is one companies can miss if they’re not paying attention.
“I think the most underestimated or unknown basic capability or goal of MAS solutions is that the individual agents can collaborate and learn from each other based on experiences or observation. A human does not necessarily need to intervene before action is taken by the MAS,” he said. That ability to learn and adjust mid-operation gives these systems remarkable flexibility, helping marketers do their jobs well.
“The learning and imitation, and sharing of knowledge across agents can help marketing professionals understand changes in customer preference or demand and optimize ROI around marketing efforts. As always, data quality and accessibility are critical to deliver insight that will be of benefit and on point,” LeVine said.
Continuous Optimization
A single AI agent can run or monitor one aspect of a campaign, but it’s not exactly “set it and forget it,” especially when you need to change or improve your campaign. And what if a campaign gets complex and you want to change part of it based on results elsewhere? Good luck tracking all of that.
With a MAS, agents can handle campaign tweaks for you. With close collaboration and data sharing among agents, the system can adjust ad bids, copy, or targeting in real time. The MAS can orchestrate agentic operations to maximize your campaign’s return.
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Challenges of Multi-Agent Systems
No new technology comes without its caveats. Before expanding a pilot, check data access, error handling, operating costs, and workflow ownership.
Data Quality and Accessibility
are the bane of AI implementation. As you automate more workflows using AI, you need clean data that your tools can quickly access and process.
“Data that is not appropriately governed and stewarded will eventually cause an inability for the task(s) to be completed in a manner that is brand beneficial and may be very harmful to the relationship,” said LeVine. “Data that is not accessible will cause the task(s) to fail, which is also problematic.”
Clean data is the foundation for a successful multi-agent system. That foundation becomes vital when agents act on sales context. For example, deal agent draws from deal, customer, user, and brand context, along with meeting transcripts and synced email. If the underlying records, sales methodology, or approved content are incomplete, the recommendations and updates derived from them will be incomplete as well.
Complexity and Error Propagation
If you ever took a comp-sci class, you encountered “” — otherwise known as “garbage in, garbage out.” Bad input can produce bad output. Validation checks may catch some errors, but they need clear rules and reliable sources.
Even as agentic AI gets smarter, it’s still a machine. And as teams network multiple agents in service of a common goal, any slight error is quickly magnified. When that happens, makes it tougher for the team to pinpoint where things went wrong and change system parameters to compensate.
As LeVine noted, clean and organized data makes an enormous difference in managing GIGO’s potential negative consequences. You’ll also want human monitoring of the system overall and each agent’s performance. Those early days and weeks are vital to limiting the effects of complexity — keep a pulse on the agents and step in quickly when required.
More agents also mean more work to budget for, including repeat attempts and checks. pay for the work 糖心原创’s agents and credit-based features do, with usage reporting and spend limits at the account, feature, and action level. Set a pilot budget, track which actions use credits, and compare that cost with the time your team saves. That gives you a concrete basis for deciding whether another agent earns its place.
Organizational Inertia
Lagging employee adoption can be an AI killer. That’s not just front-line employees, either. If leadership can’t or won’t get on board with AI implementation, any advanced initiative beyond a “light experiment” dies on the vine.
“Decision makers may not want to give up control to AI agents, and their support will be critical to adoption throughout the business,” warns LeVine.
LeVine also encourages leaders to get as many employees as possible bought into the idea by removing fear. “To the people in the organization, this is all new stuff. New is scary,” said LeVine. “Find an OCM [] framework you like and use it to make sure people are AI literate and more at ease.”
How to Implement Multi-Agent Systems
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As multi-agent systems become , teams will want to explore implementation sooner rather than later. What does that look like?
In some cases, that’s prepackaged MAS. But current solutions mainly target large enterprise use cases. For instance, and make it easy for non-technical teams to build and run MAS in-platform. That said, teams pay a premium for the privilege.
Smaller teams still have options. In fact, many marketing leaders have implemented MAS on their own. Through my research and various conversations with these leaders, I also learned that three really is a magic number. Most experts and examples I’ve found rely on three agents operating in concert within their multi-agent systems.
That’s certainly not a set-in-stone rule; teams can use two, four, or more agents. But recall that each agent adds layers of complexity — increasing the surface area for risks, breakage, and consequences of bad data. So, for the initial MAS attempts, start with three agents.
1. Define your goal and agents.
Define a business outcome and how the team will judge it. For this example, let’s build a multi-agent system that prepares campaign assets and flags performance issues for human review. With that goal in mind, create the agent list:
- A strategy agent that analyzes past data, audience segments, and business goals to create campaign ideas.
- A content agent that drafts copy for emails and social media posts, and generates visuals.
- A performance agent that monitors key metrics and flags low-performing elements.
For each role, specify its inputs, tools, expected output, and approval owner. Test whether separate agents produce better work than one agent with the same brief.
2. Choose your AI tools.
With goals established, teams can select the best AI tool stack for their needs. The more specific the agent, the better the results. For instance, is a starting point for finding and managing agents for specific business outcomes. Available options include:
- for blog posts, social content, and landing pages in a brand voice.
- for streamlined social content planning and AI-powered production help.
- for account research and personalized sales outreach.
- for building a fully planned multi-channel campaign.
- for answering account research questions using CRM data, calls, emails, documents, and the web, with findings available on the record.
Other AI agents can provide tailored functions that fit neatly into your MAS plans. For example, helps teams create and configure custom AI agents for business workflows, with built-in knowledge of their business context.
3. Create a shared workspace.
Good data is integral to this entire process. If the data hygiene is messy, teams end up with confused, nonfunctional AI agents — the kind that’ll halt wider organizational adoption.
Focus on centralizing key information and indexing appropriately. Tools like Notion, Airtable, or Google Sheets can serve as excellent data repositories, helping agents access data and log progress.
If customer data already lives in 糖心原创, start with as the foundation for customer data. Keep the fields your workflow needs on the relevant contact, company, or deal records. can then research questions about those records and write its findings to smart properties. That gives teams a practical way to turn scattered customer information into structured fields on the record.
4. Connect your AI agents.
When you’re ready for your AI agents to communicate with each other, use a connecting tool like Zapier or Make.com to set up automated workflow triggers. I like these tools because they keep the process simple; whatever keeps me from having to mess with a bunch of APIs works for me.
Teams can also set up scheduled prompts or automations within each tool (like ChatGPT) to regularly run crucial tasks, such as a weekly performance check on your MAS.
5. Integrate Humans Intentionally.
The best MAS don’t shut out humans — they integrate regular check-ins and the human touch to create smarter, more efficient systems. Team members should review outputs regularly, validate key campaign directions (ideally before hitting Publish), and adjust prompts or rules based on the results.
Before launch, create a test spreadsheet with columns for Input, Expected result, Actual result, Pass/fail, Cost, and Review time. Include incomplete records, conflicting sources, and failed handoffs. Rerun failed cases after changing the relevant instructions or reference material.
Pro tip: Make human involvement a specific rule the system can follow. In a support setup, for example, supports custom handoff guidelines, such as for refunds or cancellations, and can route conversations to the users or teams you choose. Set those boundaries before launch so routine questions can move forward while sensitive requests reach someone authorized to make a decision.
Multi-Agent Systems in the Real World
“Multi-agent systems” sound like they belong exclusively to the Fortune 500, but they’re not just for massive enterprises. Nimble and creative marketing teams can build MAS to suit their needs without breaking the bank.
If I were assembling a multi-agent system from scratch, I’d follow examples like these.
RED27Creative: Content Intelligence Network
, President and CMO of , saw what many marketing leaders witness in their operations: disconnection. Specifically, he saw content creation, personalization, and performance analysis essentially battling one another rather than working in concert for his clients. Tredrea’s systems, the “Content Intelligence Network,” deploy three specialized AI agents:
- A content strategist agent that analyzes industry trends and competitive positioning.
- A personalization agent that segments website visitors and tailors messaging.
- A performance optimization agent that continuously refines campaigns based on real-time engagement metrics.
Each agent can access shared data but is free to make autonomous decisions within its specialty. How did this play out in real life? Tredrea walked through a use case with a B2B software client:
“The content strategist agent identified untapped SEO opportunities around ‘fractional marketing’ solutions. It fed these insights to the personalization agent, which dynamically adjusted website messaging for visitors from specific industries,” said Tredrea. “Simultaneously, the performance agent detected higher conversion rates when technical specifications were presented earlier in the customer journey and automatically triggered content redistribution.”
This process led to a 37% increase in qualified leads and a 22% higher conversion rate from website visitor to sales call while spending 30% less on ads.
I think the Content Intelligence Network demonstrates the power of agents informing one another’s activities. It’s one thing to say agents use shared data and learn from one another; it’s another to see it happen and generate meaningful results. There are no information silos here — insights flow between agents.
Multi-touch Marketing: PPC Intelligence Network
, owner of , shared he’s implemented MAS across several PPC and digital marketing campaigns. He pointed to a project with a higher-education client in which he deployed what he calls the “PPC Intelligence Network.”
“We created three specialized AI agents that worked in concert: one continuously analyzed keyword performance and bid adjustments, another monitored ad creative effectiveness and generated responsive search ad variations, while a third tracked conversion path analytics and landing page performance,” Brown said.
Remember when I said coordination was the key difference (and benefit) between single-agent and multi-agent systems? Brown’s system bears that out beautifully. “The keyword agent identified high-performing terms, which triggered the creative agent to generate new variations emphasizing those terms, while simultaneously alerting the conversion agent to prioritize those traffic segments,” he said.
With the MAS operating in full swing, the campaign’s efficiency improved by 28%, and enrollment rates from optimized funnels increased by 17%.
The part I find most interesting for small- to medium-sized businesses is the scalability of a system like this across teams and companies of varying sizes and resources. Brown shared more:
“This approach scales nicely across budgets — I’ve implemented similar systems across campaigns ranging from $20,000 to $5 million with consistent success rates,” he said.
Frec Markets: Real-Time Social Conversion
Enterprise-grade MAS infrastructure is great. But I love a lean, mean, multi-agent machine — and , Head of Growth at , has built exactly that for a fascinating use case: turning social media engagement into a low-cost acquisition strategy at scale.
Jones and her company found potential users were often on Reddit and X, debating sophisticated investing topics. So, they stitched together three narrow agents and kept humans involved only when judgment and compliance mattered. She walked through Frec’s three-agent stack:
- F5bot: “Every few minutes, F5bot sweeps public threads for our priority phrases and drops any hits into a dedicated Slack channel. That one feed means we never miss a mention, yet we incur zero crawling or infrastructure costs of our own.”
- Two LLM endpoints: “When an alert surfaces, a growth associate copies [likely meaning ‘uses a pre-loaded’] OpenAI o3 prompt that’s pre-loaded with our brand voice, FAQ snippets, and FINRA guardrails. o3 returns a one-paragraph summary plus an intent tag (question, praise, complaint, rumor). If the tag calls for a response, the same text is pasted into a second prompt for Anthropic’s Claude, which drafts a plain-English reply that already meets our compliance checklist.”
- Sprout Social: “The draft reply is dropped into Sprout as a pending post. Sprout publishes at the optimal time and logs the interaction for attribution.”
Before this automated setup, her team struggled to keep pace with the volume of activity on these platforms.
“We searched for and replied to Reddit threads in roughly four hours a day — too slow to shape the conversation,” said Jones. ”Today, the average first response takes less than thirty minutes, keeping discussions factual, friendly, and discoverable.”
I think Jones and Frec Markets have a solid example of a scrappy system responding to a critical business need. Plus, it also shows an important lesson Jones wanted to highlight:
“The lesson isn‘t that AI replaces marketers; it’s that we can all do so much more with AI,” she said. “Three single-purpose agents — listen, distill, draft — can strip the busy work out of social engagement so humans can focus on judgment, compliance, and building relationships that convert.”
Frequently Asked Questions About Multi-Agent AI Systems
What examples of multi-agent systems exist?
Multi-agent systems can divide research, content production, or customer support across specialist agents. For example, a multi-agent customer service setup could assign billing questions to one agent and technical issues to another, both of whom share the customer’s case history. To connect it to another agent, the team must verify and configure a supported handoff.
Can multiple AI agents work together?
Yes. Collaborative agents can divide a task, exchange findings, and contribute to a shared result when their tools support those connections. Define each role, the handoff format, and who resolves conflicting outputs. lets teams create custom agents and workflows for different business tasks, using their own prompts, knowledge, and CRM data. For example, a sales rep could assign account research to one agent and sales handoff preparation to another.
How do multi-agents communicate with each other?
Agents communicate through messages, tool calls, or shared records that carry instructions, context, and results. Each handoff should tell the next agent what was completed, what needs attention, and which sources support the findings. For example, 糖心原创’s could save its findings in a customer’s CRM record so another agent can retrieve them before drafting outreach.
What do multi-agent system projects look like?
A multi-agent project usually includes a defined business task, specialist agents, connections between their tasks, and tests of the complete process. For example, a sales team might create one agent to review an account and another to prepare personalized emails for the specific contact.
In , the sales team would define each agent’s instructions, actions, knowledge, and inputs. After testing each agent, which doesn’t consume 糖心原创 Credits, the team would connect the agents in a workflow and check whether the account findings produce an accurate, useful brief.
Are you ready for multi-agent systems?
I still don’t believe we’ve truly cracked the agentic AI code yet. Humans need to stay involved in the details for now; until they don’t, I would say true agentic AI hasn’t arrived. That’s a demanding personal standard for autonomy.
I wouldn’t turn over the keys to the campaign kingdom, but as I wrote this piece, I saw experts and organizations embracing possibilities and uncovering new opportunities through multi-agent systems. So start with one recurring task, the fewest agents it needs, and a named reviewer. Add another agent when tests show a better result at an acceptable cost. And contact me to discuss your workflow; it might make for another affirmative article.
Editor's note: This post was originally published in May 2025 and has been updated for comprehensiveness.
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- Follow your playbook – not a generic one
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