It feels like not that long ago, companies were just starting to talk about AI in their operations. You had highly specific use cases or industry needs, but the sweeping proclamations of \u201cartificial intelligence will upend business as we know it\u201d are only now feeling especially urgent.<\/p>\n\n
I blinked, and everything was ChatGPT. Blink again, and it\u2019s \u201cagentic AI<\/a>.\u201d And right as I started experimenting with AI agents, the question became, \u201cHow do AI agents work together in shared systems?\u201d<\/p>\n <\/a><\/p>\n The multi-agent AI system is yet another \u201cnext step\u201d on the road to AI adoption. But, I think it\u2019s a logical one. A single AI agent can help your marketing team, but a group of them can really<\/em> get things going.<\/p>\n Let\u2019s talk about where the tech is now and how you can bring AI agents into your organization.<\/p>\n Table of Contents<\/strong><\/p>\n What are multi-agent systems?<\/a> <\/a> <\/p>\n A multi-agent system (MAS) is a network of AI agents that operate on their own 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.<\/p>\n <\/a> <\/p>\n Multi-agent systems<\/a> operate by assigning specialized tasks to agents that interact within a shared environment. You see this structure on your human marketing team now:<\/p>\n A campaign strategist who researches the target audience and positioning. 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, but communicates within the shared framework of team goals and desired outcomes.<\/p>\n A multi-agent system runs similarly. Each agent manages its tasks but can negotiate, delegate, and learn from one another. Plus, these agents can adapt dynamically<\/a> to changes in the ecosystem without human input.<\/p>\n To help contextualize a MAS, I talked with David LeVine<\/a>, Chief Strategy and Finance Officer for Lucid Services Group<\/a>. LeVine walked me through an explanation of how a system of AI agents can work together for marketers:<\/p>\n This agent would \u201clisten\u201d to a marketer describe the campaign they want to create. The human provides target audiences, channels, goals, and creative ideas using natural language. This agent processes the information and prepares it for the MAS.<\/p>\n This agent takes the campaign data and develops multiple campaign strategies and creative direction. Along with what the human marketer provided, the agent can autonomously query other data sources and past campaign content to help craft strong strategic angles.<\/p>\n This agent can run simulated tests or develop A\/B setups to evaluate campaign assets\u2019 performance potential. It can pull information from CRM data, online surveys, or other analytics tools to pre-test campaign content before a human hits go.<\/p>\n This agent launches the campaign (with human oversight as desired). It watches for performance and how the campaign lands in the marketplace, adjusting messaging, spend, and targeting across segments and channels.<\/p>\n AI agents can accomplish a lot on their own \u2014 and, ideally, that\u2019s the goal. But, LeVine noted the value humans have in the development and deployment processes. \u201cAll of these stages would need human validation, especially early on,\u201d he said. With human support, these MAS can align and optimize a campaign\u2019s impact while reducing risks to your brand.<\/p>\n <\/a> <\/p>\n Before I dive deeper into multi-agent systems, I should note the differences between types of agentic AI now being sold to companies.<\/p>\n When I\u2019ve previously discussed agentic AI for marketing<\/a> and social media<\/a>, I shared single AI agents. These agents can work autonomously alongside your teams, usually to support a very specific function or task.<\/p>\n The name of that game is \u201coutput.\u201d You could 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 \u2014 you get something<\/em> at the end of the agentic process.<\/p>\n On marketing teams today, I think these agents operate as particularly well-educated interns. You wouldn\u2019t leave them entirely on their own (yet), but you can trust they\u2019ll do a fine job. That\u2019s especially the case with where these agents plug in best now:<\/p>\n Frontline customer support If a single AI agent is an intern, MAS is the group of interns graduating into full-time roles. A MAS still produces something \u2014 there are results from their operation. But the core difference is how these systems create that outcome.<\/p>\n MAS are designed for coordination. 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.<\/p>\n AI agents are still finding their footing within most organizations \u2014 AI adoption is happening<\/a>, albeit carefully. Multi-agent systems stretch a team\u2019s technical capabilities even further than single agents do. Still, I\u2019ve found some technical-forward marketers using MAS as a campaign manager or operating in a support capacity.<\/p>\n <\/a> <\/p>\n With power and opportunity available to your team, a multi-agent system can bring impressive benefits. Let\u2019s cover a few of the most crucial.<\/p>\n When I\u2019ve assembled teams in the past, I’ve sought experts in particular fields. That could be a marketing team with various skills like copywriting, long-form writing, and visual design. The best teams are typically greater than the sum of their parts: McKinsey research<\/a> shows cross-functional teams can achieve up to a 30% increase in efficiency.<\/p>\n A multi-agent system brings similar benefits. An agent might focus on strategy, content, or testing. While each agent operates in its prescribed function, it provides valuable data for its fellow agents in service of the team goal. That collaboration across functions removes information silos that plague human teams while speeding up problem-solving.<\/p>\n Within a MAS, agents can share knowledge and imitate effective behaviors, meaning they can learn and adjust over time. LeVine noted this feature is one that companies can miss out on if they\u2019re not paying attention.<\/p>\n \u201cI 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,\u201d he said.<\/p>\n That ability to learn and adjust mid-operation gives these systems remarkable flexibility to help marketers do their jobs well.<\/p>\n \u201cThe 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,\u201d LeVine said.<\/p>\n A single AI agent can run or monitor one aspect of a campaign, but it\u2019s not exactly \u201cset it and forget it,\u201d especially when you need to change or improve your campaign. And, as a campaign gets complex and you want to change some part based on results elsewhere? Good luck tracking all of that.<\/p>\n With a MAS, agents can handle campaign tweaks for you. With close collaboration and data-sharing between agents, your system can adjust ad bids, copy, or targeting in real time. The MAS can orchestrate<\/em> agentic operations to maximize your campaign\u2019s return.<\/p>\n <\/a> <\/p>\n No new technology comes without its caveats. A multi-agent system is certainly a new technology. But, most challenges with a MAS relate to how<\/em> your team engineers the system and adopts it operationally.<\/p>\n Data quality issues<\/a> are the bane of AI implementation. And as you automate more workflows using AI<\/a>, you need clean data that your tools can quickly access and process.<\/p>\n \u201cData 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,\u201d said LeVine. \u201cData that is not accessible will cause the task(s) to fail, which is also problematic.\u201d<\/p>\n Clean data is the foundation for a successful multi-agent system. Review your data sources and look to remove duplicate data, standardize formats, and ensure consistency across sources.<\/p>\n If you ever took a comp-sci class, you encountered \u201cGIGO<\/a>\u201d \u2014 otherwise known as \u201cgarbage in, garbage out.\u201d When you give a system bad input to start, you\u2019ll get bad output; the system has no way of discerning what\u2019s good from what\u2019s garbage.<\/p>\n Even as agentic AI gets smarter, it\u2019s still a machine. And, as you network multiple agents in service of a common goal, any slight error gets magnified quickly. When that happens, increasing complexity<\/a> makes it tougher for you and your team to pinpoint where things went wrong and change system parameters to compensate.<\/p>\n As LeVine noted, clean and organized data makes an enormous difference in managing GIGO\u2019s potential negative consequences. You\u2019ll also want human monitoring of the system overall and each agent\u2019s performance. Those early days and weeks are vital to limiting the effects of complexity \u2014 keep a pulse on your agents and step in quickly when required.<\/p>\n I\u2019ve talked before about lagging employee adoption being the AI killer. That\u2019s not just front-line employees, either. If leadership can\u2019t or won\u2019t get on board with AI implementation, any advanced initiative beyond a \u201clight experiment\u201d dies on the vine.<\/p>\n \u201cDecision makers may not want to give up control to AI agents, and their support will be critical to adoption throughout the business,\u201d warns LeVine.<\/p>\n He also encourages you to get as many employees bought into the idea as possible by removing the fear. \u201cTo the people in the organization, this is all new stuff. New is scary,\u201d said LeVine. \u201cFind an OCM [organizational change management<\/a>] framework you like and use it to make sure people are AI literate and more at ease.\u201d<\/p>\n <\/a> <\/p>\n As multi-agent systems become bigger players in company operations<\/a>, you\u2019ll want to explore implementation sooner rather than later. What does that look like?<\/p>\n In some cases, that\u2019s prepacked MAS. But, current solutions mainly target large enterprise use cases<\/a>. For instance, Accenture\u2019s AI Refinery<\/a> and Salesforce\u2019s Agentforce<\/a> make it easy for non-technical teams to build and run MAS in-platform. That said, you\u2019ll pay a premium for the privilege.<\/p>\n If you don\u2019t have enterprise funding, you 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<\/a>. Most experts and examples I\u2019ve found rely on three agents operating in concert within their multi-agent systems.<\/p>\n That\u2019s certainly not a set-in-stone rule; you can use two, four, or more agents. But recall that each agent adds layers of complexity \u2014 increasing the surface area for risks, breakage, and consequences of bad data. So, for your initial MAS attempts, start with three agents.<\/p>\n With that goal in mind, let\u2019s chat about where you start.<\/p>\n While agentic AI can do a lot of work on its own, you still want an overarching goal or purpose for your MAS. For our example, let\u2019s build a multi-agent system focused on helping launch and monitor a marketing campaign.<\/p>\n With that goal in mind, we can create our agent list:<\/p>\n A strategy agent that analyzes past data, audience segments, and business goals to create campaign ideas. With goals established, you can select the best AI tool stack for your needs. I find that the more specific the agent, the better the results. For instance, HubSpot\u2019s Breeze AI<\/a> agents include:<\/p>\n The Content Agent<\/a> for tailored blogs, landing pages, and other longer-form content creation. Other AI agents<\/a> can provide tailored functions that fit neatly into your MAS plans. Remember: the agents themselves are only part of the answer; you need to build strong connections between them and feed them with high-quality data.<\/p>\n I truly cannot stress enough how important good data is to this entire process. If your data hygiene is messy, you\u2019ll end up with confused, nonfunctional AI agents \u2014 the kind that\u2019ll disappoint your teams and halt wider organizational adoption.<\/p>\n You don\u2019t need perfection to start, but focus on centralizing key information and indexing appropriately. Tools like Notion, Airtable, or Google Sheets can serve as excellent data repositories to help agents access data and log progress.<\/p>\n When you\u2019re ready for your AI agents to communicate with each other, use a connecting tool like Zapier or Make.com<\/a> 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.<\/p>\n You can also set up scheduled prompts or automations within each tool (like ChatGPT) to regularly run crucial tasks like a weekly performance check on your MAS.<\/p>\n The best MAS don\u2019t shut out humans \u2014 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 you hit Publish), and adjust prompts or rules based on your results.<\/p>\n In this way, a multi-agent system operates as a team within your team. Treat your AI team with good data and clear direction, and you can unlock greater results.<\/p>\n <\/a> <\/p>\n \u201cMulti-agent systems\u201d sound like they belong exclusively to the Fortune 500, but they\u2019re not just for massive enterprises. Nimble and creative marketing teams can build MAS to suit their needs without breaking the bank.<\/p>\n If I were assembling a multi-agent system from scratch, I\u2019d follow examples like these.<\/p>\n Kiel Tredrea<\/a>, President & CMO of RED27Creative<\/a>, saw what many marketing leaders witness in their operations: disconnection. Specifically, he saw content creation, personalization, and performance analysis basically battling one another instead of working in concert for his clients.<\/p>\n Tredrea\u2019s systems, the \u201cContent Intelligence Network,\u201d deploys three specialized AI agents:<\/p>\n A content strategist agent that analyzes industry trends and competitive positioning. 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 me through a use case with a B2B software client:<\/p>\n \u201cThe content strategist agent identified untapped SEO opportunities around \u2018fractional marketing\u2019 solutions. It fed these insights to the personalization agent, which dynamically adjusted website messaging for visitors from specific industries,\u201d said Tredrea. \u201cSimultaneously, the performance agent detected higher conversion rates when technical specifications were presented earlier in the customer journey and automatically triggered content redistribution.”<\/p>\n 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.<\/p>\n I think the Content Intelligence Network shows the power of agents informing each other\u2019s activities. It\u2019s one thing to say<\/em> agents use shared data and learn from one another; it\u2019s another to see it happen and<\/em> generate meaningful results. There are no information silos here \u2014 insights flow between agents.<\/p>\n Milton Brown<\/a>, owner of Multi Touch Marketing<\/a>, shared he\u2019s implemented MAS across several PPC and digital marketing campaigns. He pointed me to a project with a higher education client where he deployed what he calls the \u201cPPC Intelligence Network.\u201d<\/p>\n “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,\u201d Brown said.<\/p>\n Remember when I said coordination was the key difference (and benefit) between single-agent and multi-agent systems? Brown\u2019s system bears that out beautifully.<\/p>\n “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,\u201d he said.<\/p>\n With the MAS operating in full swing, the campaign\u2019s efficiency improved by 28%, and enrollment rates from optimized funnels increased by 17%.<\/p>\n The part I find most interesting for small to medium businesses is the scalability of a system like this across teams and companies of various sizes and resources. Brown shared more:<\/p>\n \u201cThis approach scales nicely across budgets \u2014 I’ve implemented similar systems across campaigns ranging from $20,000 to $5 million with consistent success rates,\u201d he said.<\/p>\n Enterprise-grade MAS infrastructure is great. But I love a lean, mean, multi-agent machine \u2014 and Amberly Jones<\/a>, Head of Growth at Frec Markets<\/a>, has built exactly that for a fascinating use case: turning social media engagement into a low-cost acquisition strategy at scale.<\/p>\n 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.<\/p>\n She walked me through Frec\u2019s three-agent stack:<\/p>\n F5bot<\/strong>: \u201cEvery 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.\u201d <\/p>\n Two LLM endpoints<\/strong>: \u201cWhen an alert surfaces, a growth associate copies [likely meaning \u2018uses a pre-loaded\u2019] OpenAI o3 prompt that\u2018s pre-loaded with our brand voice, FAQ snippets, and FINRA guardrails. o3 returns a one-paragraph summary plus an intent tag (question, praise, complaint, rumour). If the tag calls for a response, the same text is pasted into a second prompt for Anthropic\u2019s Claude, which drafts a plain-English reply that already meets our compliance checklist.\u201d <\/p>\n Sprout Social<\/strong>: \u201cThe draft reply is dropped into Sprout as a pending post. Sprout publishes at the optimal time and logs the interaction for attribution.\u201d <\/p>\n Before this automated setup, her team struggled to keep pace with the volume of activity on these platforms.<\/p>\n \u201cWe searched for and replied to Reddit threads in roughly four hours a day \u2014 too slow to shape the conversation,\u201d said Jones. \u201dToday, the average first response takes less than thirty minutes, keeping discussions factual, friendly, and discoverable.”<\/p>\n 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:<\/p>\n \u201cThe lesson isn\u2018t that AI replaces marketers; it\u2019s that we can all do so much more with AI,\u201d she said. \u201cThree single-purpose agents \u2014 listen, distill, draft \u2014 can strip the busy work out of social engagement so humans can focus on judgment, compliance, and building relationships that convert.\u201d<\/p>\n
\n How do multi-agent systems work?<\/a>
\n Multi-Agent Systems vs. Single AI Agents<\/a>
\n Benefits of Multi-Agent Systems<\/a>
\n Challenges of Multi-Agent Systems<\/a>
\n How to Implement Multi-Agent Systems<\/a>
\n Multi-Agent Systems in the Real World<\/a> <\/p>\nWhat are multi-agent systems?<\/h2>\n
How do multi-agent systems work?<\/h2>\n
\n A copywriter who crafts content to reach those audiences.
\n A visual designer who catches people\u2019s attention with images and video. <\/p>\nAgent 1: Intake & Planning<\/h3>\n
Agent 2: Ideation & Development<\/h3>\n
Agent 3: Testing & Refinement<\/h3>\n
Agent 4: Execution & Monitoring<\/h3>\n
Continuous Human Oversight<\/h3>\n
Multi-Agent Systems vs. Single AI Agents<\/h2>\n
Single AI Agents: Output<\/h3>\n
\n Content creation
\n Campaign optimization
\n Data analysis <\/p>\nMulti-Agent Systems: Coordination<\/h3>\n
Benefits of Multi-Agent Systems<\/h2>\n
Cross-Functional Collaboration<\/h3>\n
Learning and Adjusting On-the-Go<\/h3>\n
Continuous Optimization<\/h3>\n
Challenges of Multi-Agent Systems<\/h2>\n
Data Quality and Accessibility<\/h3>\n
Complexity and Error Propagation<\/h3>\n
Organizational Inertia<\/h3>\n
How to Implement Multi-Agent Systems<\/h2>\n\n
1. Define your goal and agents.<\/h3>\n
\n A content agent that drafts copy for emails and social media posts, and generates visuals.
\n A performance agent that monitors our key metrics and flags low-performing elements. <\/p>\n2. Choose your AI tools.<\/h3>\n
\n The Social Media Agent<\/a> for streamlined social content planning and AI-powered production help.
\n The Prospecting Agent<\/a> for researching target audiences and building personalized outreach campaigns. <\/p>\n3. Create a shared workspace.<\/h3>\n
4. Connect your AI agents.<\/h3>\n
5. Integrate humans intentionally.<\/h3>\n
Multi-Agent Systems in the Real World<\/h2>\n
RED27Creative: Content Intelligence Network<\/h3>\n
\n A personalization agent that segments website visitors and tailors messaging.
\n A performance optimization agent that continuously refines campaigns based on real-time engagement metrics. <\/p>\nMulti-touch Marketing: PPC Intelligence Network<\/h3>\n
Frec Markets: Real-Time Social Conversion<\/h3>\n