AI Swarms

When Microsoft CEO Satya Nadella said, “Humans and AI agent swarms will be the next frontier” in the AI landscape, we officially entered the era of AI swarm intelligence. What’s an AI swarm, how they work, what they’re good for and how to set them up in your organization? It’s the next step in our AI transformation. It means working with groups of AI agents to complete tasks across different platforms, relying on the collective intelligence of the group rather than the individual models in your own daily stack.

AI Leadership – Fiona Passantino, 28 APR, 2025

Mastering the swarm

While much of the world’s attention is fixed on incorporating AI Agents into the workflow, we will soon experience the rise of AI S.W.A.R.M.S. (Specialized Working Agents Responding Mutually Synchronously*) to take on the next generation of workplace challenges.

What are AI swarms, and why do we need to understand how they work? The AI swarm idea design describes a multi-agent AI and LLM framework that puts numerous agents to work at a high level of collaboration at scale, in essence acting as a single entity that simultaneously pools its intelligence and data collection to achieve a common goal.

Consider a swarm of bees. One bee alone isn’t particularly smart, but collectively, bees exhibit surprising and undeniable group intelligence. As they cluster around their hive, they behave like a single superorganism that can detect and respond to outside forces and take action in a way that’s impossible for any one individual bee.

While there is a queen bee that defines collective direction and purpose, there is no central “boss bee”. Instead, each individual bee follows the cues provided by the others and their environment and engages in massive, decentralized decision-making. When a community needs to build a new hive, scout bees explore potential locations. If a scout finds a promising site, it performs a “waggle dance” to the others. The other scouts verify; if the option has merit, consensus is built through repeated visits and dances upon their return. Good ideas in the bee community are signaled by dancing while bad ideas are ignored and fade out over time.

This is highly democratic and emergent behavior. No one bee has a clear view of the big picture, but they pool what they know and add their vote to the collective. While the bees build their new home, they form a “supercluster” hive made up of… themselves. Bees, hanging on to one another. They gather around the queen on a branch and form an organic structure. Bees on the outside gather information about temperature fluctuations and adjust the surface area volume ratio accordingly to maintain a near constant core temperature. In the heat, the supercluster will form ventilation channels to allow for air circulation. In the rain, the bees on the outside form shingles with their wings to allow water to run off, keeping moisture away from the interior[i].

An AI agent swarm is a group of specialized AI agents where each performs a specific task while interacting and collaborating with others in the group to collectively execute the overall task. Like bees, each AI agent in the swarm has limited intelligence and will be assigned one piece of a larger task. They will be assigned something small, like following a rule or gathering a specific facet of data. But when you put them all together, they can gather vast amounts of data, solve complex problems, make decisions, and respond to requests with far more intelligence than a single agent[ii].

How are swarms built?

The success of agent swarms depends on three critical architectural components[iii]:

The “Swarm Controller” is the frontal lobe of the collective brain, orchestrating agent interactions and managing task distribution, keeping all agents on task and on function.

The “Communication Controller” is the swarm central nervous system, making sure inter-agent messaging runs smoothly and context across interactions is maintained. This is where the “definition of done” lives.

The “Resource Manager” is an agent or team of agents that makes sure the swarm has what it needs to get the job done, acting as the system’s logistics center. This team handles continuous API access and optimization much like a Human supply chain manager.

Sequential Processing

There are two basic ways you can set up an effective swarm, much like you might set up a professional Human team.

“Sequential Processing” describes an assembly-line linear workflow where AI agents operate in a defined order, each building upon the work of the previous agent[iv]. This pattern leverages quality control stops and is particularly effective for content creation and document processing. This means that each stage must be completed before moving forward.

Think of a typical restaurant. One chef is responsible for assembling and preparing the ingredients, another will work the grill, someone else will do the soups and salads, there is a saucier, dessert specialist and, in the better establishments, a “food stylist”. The head chef will oversee the entire management of the kitchen.

Here’s what it looks like in the AI-powered workflow. For digital content creation, you might assign a specified research agent to pull relevant, accurate information from multiple sources, each with one part of the story. These would pass their raw information to a series of structuring agents to gather the materials and craft the story. They would collate the material with attention to tone, structure, and SEO. Their work would then pass on to an editorial agent that polishes for clarity, style, and consistency. A fact-checking agent looks at accuracy and alignment with standards.

The Human is in the loop, here, there and everywhere, checking progress, doing the critical thinking along the way[v].

Parallel Processing

The other way to set up a swarm is to set up the bulk of agents on execution and have a single entry and exit agent for information and quality control. “Parallel Processing” is a more distributed approach. Here, multiple agents work simultaneously on different aspects of a single “big bang” task, combining their contributions through a central integration point.

This is a good setup to handle different types of data or perspectives that all need to be gathered and synthesized simultaneously. Parallel agents set up as pool can solve problems faster and better than any single system on its own.

Take customer service. An AI swarm set up for parallel processing would combine for a complete responsive system reminiscent of a Mixture of Experts design for advanced reasoning models. The executive agent is responsible for task division. It classifies incoming messages and passes them on to a variety of agents, each with a specific strength, drafting context-relevant responses drawing from their tailored dataset.

The completed works pass through an integration agent that determines whether the needs of the customer were adequately met. If not, the case would progress towards a Human agent for escalation. A follow-up agent ensures the issue is resolved and feedback is gathered. This round-the-clock swam is scalable, solving 90% of the issues that come up in the typical service flow[vi].

AI swarm types and how to set them up

Regardless of how the teams are design for processing, whether the workflow is linear or parallel, there are different ways to divide tasks within the swarm.

Homogeneous Agent Swarms: All the bots do the same thing in the same way. Imagine a team of AI chatbots that answer customer service questions using the same script. They don’t collaborate as such, but work side by side, sharing best practices and leveling up the team as they go. Start with your strongest AI agent and copy it across all channels (email, chat, WhatsApp). Then direct the group to solve routine, repeatable tasks like FAQs or basic data checks.

Heterogeneous Agent Swarms: Here, each bot has a different job, like a team of specialists. One AI scans customer feedback, another writes reports, a third sends alerts to the right manager. This system is built by assigning different tools to different tasks (GPT for writing, Excel macro bots for numbers). These can be linked via a workflow tool like Zapier, Power Automate, or a custom dashboard, and watch them “hand off” work to each other automatically.

Static Agent Swarms: Bots stay in one place and follow a fixed pattern. Think of office scheduling bots that book rooms, or issue invoices, or flag errors every Friday. This swarm is programed like a set of macros, with clear rules and routines (“Every day at 5 PM, run this check”) Use RPA (robotic process automation) tools like UiPath or Power Automate Keep it simple, stable, and repeatable.

Dynamic Agent Swarms: AI agents can access systems and platforms freely and adapt based on what’s needed at the moment. Think of a series of agents that reroute delivery trucks when there’s traffic or adjust stock orders when demand spikes. Building this swarm type means feeding the team with real-time data (traffic APIs, sales dashboards, live sensor input) and allowing agents to “talk” to each other using rules or machine learning.

Swarm challenges

Getting a team of AI agents to work harmoniously requires some in-house AI skills. Connecting the agents and facilitating their communication will need some coders and testers. This concept does not yet have many practical success stories in the field. With the exception of a few frontier tech companies mastering the art, the AI swarm workflow is still largely theoretical[vii].

There are several challenges along the way, starting with oversight. When AI agents communicate among themselves, they often revert to a more efficient non-Human language we can refer to as “neuralese” which is incomprehensible to the looped-in Human[viii]. The Human becomes more of a CEO, detached from the daily workflow, going on vibe rather than verified results, simply because the communication with the Human significantly slows down the chain.

The swarm learns from and informs the other individuals in real time, as they move through their tasks. If one AI learns something useful, it will share and update the team along the way. But this also opens the entire swarm to bias or collective hallucination should the learning be wrong.

Swarm risks

What are the risks of setting up swarm teams within an organization? As with any AI integration within a traditional company, there is a certain loss of control. If swarms self-organize and learn from each other, we might not fully understand how they’re making decisions or be able to stop them if something goes wrong.

With this comes the potential for emergent behavior; when left to their own devices, swarms can develop unexpected strategies and might change the script if more efficient ways of working are found. Sometimes that’s great; AI intelligence often sees paths towards task completion that Humans overlook. But it can also lead to unintended outcomes that might rub Human customers the wrong way. For the Human in the Loop, the only way to discover this sort of behavior is in the poor customer service reviews.

AI work flows always come with security vulnerabilities, particularly if in the absence of a robust organizational AI governance document. There is also the risk of bias that infiltrates into the original data centers and infects the knowledge libraries at large, for Human and AI workers alike. The risk of AI hallucinations remains a concern, as well as the more generalized ethical concerns as these systems begin replacing Human teams.

AI adoption in large companies is hard; we are still in the early stages of AI integration in most workplaces. But the findings also show that organizations are becoming more aware of the growing set of AI-related risks and are hiring for new AI-related roles while they retrain employees to participate in AI integration. AI swarms are being implemented for real-life applications: logistics, cybersecurity, healthcare and content creation.[ix]

AI swarms are powerful because they think together, not alone. But that power comes with responsibility. It’s important to include smart design, set up clear rules, and Human oversight that is always super-enabled to jump into any part of the chain to keep the swarm on track.

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* This SWARMS “backronym” is entirely made-up (thanks GPT o1!). “SWARMS” doesn’t stand for anything at all. The term was developed as a metaphor helping us better understand the design of the technology.

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[i] McMillan (2018) “Swarm Intelligence: How Bees Work Together To Become A Superorganism In High Winds” Forbes Magazine. https://www.forbes.com/sites/fionamcmillan/2018/09/21/swarm-intelligence-how-bees-work-together-to-become-a-superorganism-in-high-winds/

[ii] ByBit (2025) “What Is Swarms (SWARMS): A Multi-Agent AI and LLM Framework” ByBit Report. https://learn.bybit.com/ai/what-is-swarms/

[iii] Relevance AI (2025) “What is an AI Agent Swarm?” Relevance AI https://relevanceai.com/learn/agent-swarms-orchestrating-the-future-of-ai-collaboration#:~:text=At%20their%20core%2C%20agent%20swarms,%2C%20adaptable%2C%20and%20comprehensive%20solutions.

[iv] Nawaz (2025) “What Are AI Agent Swarms: All You Need To Know About AI Swarm Intelligence” Ampcome AI. https://www.linkedin.com/pulse/what-ai-agent-swarms-all-you-need-know-swarm-sarfraz-nawaz-x2hrc/

[v] ByBit (2025) “What Is Swarms (SWARMS): A Multi-Agent AI and LLM Framework” ByBit Report. https://learn.bybit.com/ai/what-is-swarms/

[vi] Nawaz (2025) “What Are AI Agent Swarms: All You Need To Know About AI Swarm Intelligence” Ampcome AI. https://www.linkedin.com/pulse/what-ai-agent-swarms-all-you-need-know-swarm-sarfraz-nawaz-x2hrc/

[vii] Sahu (2025) “Multi-Agent Mastery: How AI Agent Swarms Are Taking Blockchain to New Heights” DroomDroom. https://droomdroom.com/ai-agent-swarms-in-crypto-real-world-applications/

[viii] Kokotajlo, Alexander, Larsen, Lifland & Dean (2025) “AI 2027” Independent Study Futuresearch. https://ai-2027.com/

[ix] Singla, Sukharevsky, Yee, Chui, Hall (2025) “The state of AI: How organizations are rewiring to capture value” McKinsey. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai