What are agent swarms and how do they work?

AI agent swarms are considered a form of multi-agent system. Rather than having several agents in the same system, however, the term “swarm” often emphasizes collaboration among multiple, relatively autonomous agents. The name is partly inspired by systems in nature such as insect colonies, where many individuals work together to accomplish complex tasks.

The defining idea is coordination. A swarm needs some way to determine what work needs to be done, assign that work and bring the results together. Depending on its design, coordination can come from a central orchestrator, a hierarchy of agents or more decentralized interactions.

For example, one agent might plan a project while other agents handle individual tasks. A separate agent could review the work before another agent combines the results. Agents can work sequentially or in parallel. They can also share information, delegate tasks and respond to the work of other agents. Strictly decentralized swarms rely primarily on local interactions and shared signals rather than a central controller.

Agent swarms can be useful when a task contains distinct pieces that can be delegated or when different agents can contribute complementary capabilities. However, adding more agents doesn’t automatically make a system better. Multiple agents introduce more coordination, communication and resource use, so the value of a swarm depends on whether those additional capabilities justify the added complexity.

AI agent swarms can change how an organization structures and manages AI-assisted work. Instead of relying on a single AI assistant to handle an entire process, organizations can distribute parts of a workflow across multiple agents. Organizations can also embed these agent workflows into existing business processes and applications. Implementation can affect how work is assigned, how tasks move through a process and how much human collaboration is needed.

One potential impact is the scale of AI-assisted operations. When a task can be divided into independent subtasks, multiple agents can work concurrently rather than requiring one agent loop to process each part in sequence.

A swarm can distribute a larger workload across multiple agents, allowing independent tasks to happen at the same time. This coordination can be useful for complex or high-volume workflows.

Swarms can also change how specialized work is organized. Different agents can handle research, analysis or review based on their capabilities. This way, an AI workflow can be divided into roles with different responsibilities rather than relying on one agent to perform every part of the process.

The impact isn’t always positive. More agents create more moving parts. Organizations need to manage how agents communicate, how work is handed off and what happens when agents produce conflicting results. Running multiple agents can also increase costs, particularly when agents frequently interact.

Swarms can also change the role of people within an AI-enabled workflow. Employees might spend more time defining AI goals, reviewing results and managing exceptions instead of directly completing each task. That shift of responsibilities can reduce human work in some processes while creating a need for more oversight and management.

These changes affect how organizations structure workflows, allocate resources and involve people in AI-assisted work. Those effects are important to consider when evaluating whether a swarm fits a particular task.

AI agent swarms and multi-agent systems both involve multiple AI agents working within the same system. The terms are sometimes used interchangeably, but multi-agent system is generally the broader term.

• A multi-agent system can include agents that work independently, follow a predefined workflow or interact with one another. The agents can have different roles and capabilities, but they don’t necessarily need to collaborate closely.
  
  
• An agent swarm describes a more collaborative arrangement. Agents can divide a larger task, specialize in different types of work, share information and respond to the actions or results of other agents. The degree of coordination can vary. Some swarms rely on a central agent or orchestrator while others allow agents to interact more directly.

The distinction isn’t always clear. A system with several independent agents can be a multi-agent system without functioning as a swarm, while a swarm can be organized in several different ways. In practice, the terms often overlap, with “swarm” placing more emphasis on coordinated agent activity.

An AI agent swarm organizes work so that multiple agents can contribute to a shared goal. The exact process depends on the swarm’s design, but most systems need to address four questions:

1. What needs to be done?
2. Who should do it?
3. How should the results come back together?
4. What governance controls determine what each agent can access, decide and change?

Consider a company that wants to research a new market. A swarm might have one agent develop the research plan, several agents investigate different aspects of the market in parallel and another agent review and combine their findings. This arrangement allows different parts of the research to happen simultaneously while giving each agent a focused area of responsibility.

The swarm first needs to break the overall goal into smaller tasks. A planning agent might do this explicitly, or the tasks might be defined as part of the workflow in advance.

The way work is divided depends on the problem. Some tasks can be handled independently and sent to several agents at once. Other tasks depend on earlier results and need to happen in sequence. A research agent, for example, might need to complete its work before another agent can analyze the findings.

Agents can also take on specialized roles. One agent might focus on gathering information while another analyzes data or checks the quality of the work. A system prompt can define an agent’s instructions or behavior, while its tools and other capabilities determine what work the agent can perform. Specialization allows the swarm to assign work according to an agent’s instructions, tools or capabilities.

Once work is divided, agents need access to the information required to complete their tasks. That information can move between agents in different ways.

Agents might send messages directly to one another. They can also use function calling to employ tools or external systems and pass the resulting information into the workflow. A shared workspace or memory can allow multiple agents to access the same information.

Agents can also pass along files, research findings, code or other outputs as one task leads into another. Retrieval systems can also supply agents with external context from sources such as documents, databases or connected knowledge bases. Approaches such as vector search and graph RAG can retrieve relevant information in different ways, depending on how the underlying data is structured.

The amount of shared information is important. Giving every agent the entire history of a task can consume its context window and increase the amount of information the system needs to process. Giving an agent too little context can lead to incomplete or inconsistent work. Swarm designs need a way to provide agents with the information they need without creating unnecessary overhead.

Communication alone doesn’t make a swarm effective. The system also needs to coordinate what happens next.

A central agent or agent orchestration layer might assign tasks, monitor progress and decide when results are ready to combine. In larger systems, observability can help teams track agent activity, tool calls, handoffs and results. In other designs, agents can determine their next actions based on messages, shared state or the results of other agents.

Coordination can also involve reviewing work. An agent might check another agent’s output before it becomes part of the result, or a workflow might require human approval before an agent acts. If the work is incomplete, the swarm can send the task back for another attempt or assign it to a different agent.

The final step is bringing the individual outputs together. One agent might synthesize the findings into a final report, while another system might combine outputs automatically. The goal is to turn many separate pieces of work into one result that addresses the original task.

An agent swarm isn’t simply a collection of AI agents. It is a coordinated process in which work is divided, information moves between agents and individual results contribute to a shared objective. The way those activities are organized determines how the swarm behaves and what kinds of tasks it can handle.

The architecture of an AI agent swarm varies depending on the task, the relationships between tasks and the level of coordination required. These choices form part of a broader multi-agent architecture, with some patterns describing how work is executed and others describing how agents are organized or how they collaborate and make decisions. These patterns can also be combined within the same swarm.

• Sequential: Agents handle work one step at a time, with each agent using the previous agent’s output. For example, one agent might gather information, another might analyze it and a third might prepare the final report. This approach fits tasks where later steps depend on earlier ones, but it limits the opportunity to perform work in parallel.
  
  
• Parallel: Multiple agents work on separate parts of a task at the same time before their results are combined. A market research swarm, for example, could have different agents investigate competitors, customers and industry trends. Parallel execution can shorten a workflow when tasks are genuinely independent and the coordination overhead does not outweigh the time saved.
  
  
• Iterative: Agents repeat or refine work based on the results of earlier steps. An agent might produce an initial output, another might review it and the work might return to the original agent for revision. This pattern can be useful when the quality of an output depends on repeated evaluation and refinement.

• Centralized or manager-led: A central agent or orchestration layer directs the work, assigns tasks and collects results from other agents. This structure can provide a clear point of control, but the coordinating agent or system can also become a dependency for the rest of the workflow.
  
  
• Hierarchical: Agents are organized into levels, with higher-level agents assigning work to lower-level agents. A coordinator might break a larger goal into tasks, while other agents manage smaller groups of specialized agents. Hierarchical structures can help organize complex workflows but add additional coordination layers.
  
  
• Decentralized or peer-to-peer: Agents coordinate more directly without relying on a single agent to manage every interaction. Agents can respond to messages, shared state or the results of other agents and determine their next actions based on the information available to them. This can reduce reliance on a central coordinator but can make coordination and control more difficult.

• Handoffs: One agent transfers responsibility for a task to another agent when a different capability or role is needed. For example, a triage agent might route a request to a specialized agent that can handle it. Handoffs allow different agents to take responsibility for different parts of a workflow.
  
  
• Supervisor-worker: A supervisor agent delegates tasks to worker agents and evaluates or combines their results. The supervisor can determine what work is needed, assign it to appropriate agents and decide what happens next.
  
  
• Debate or critique: Multiple agents examine a problem or an existing output from different perspectives. One agent might challenge another’s reasoning or identify weaknesses in its response. This can be useful when exposing disagreements or possible errors is more valuable than relying on a single response.
  
  
• Voting or consensus: Multiple agents produce recommendations or judgments that are then compared to reach a combined result. This approach can be useful when independent agreement is informative, although agreement between agents does not guarantee that the result is correct.
  
  
• Shared-state collaboration: Agents work from a shared representation of the task, such as a workspace, memory or other shared state. Agents can read and update information as the workflow progresses rather than passing every result directly from one agent to another. This can support collaboration across more complex workflows but requires careful management of what information agents can access and change.

Frameworks can implement and combine these patterns in different ways. The OpenAI Agents SDK provides tools for building agents, managing handoffs and coordinating multi-agent workflows. AutoGen provides components for building and coordinating multi-agent applications. LangChain provides tools for building applications around language models and agents, including multi-agent workflows. These frameworks use different approaches, so the patterns they support do not represent a universal architecture taxonomy.

These patterns are not mutually exclusive. A swarm might use a hierarchical coordination structure to assign work, execute independent tasks in parallel and use iterative review before producing a final result. The architecture and execution patterns should therefore be understood as design choices that can be combined according to the requirements of the workflow.

AI agent swarms can offer advantages when a workflow benefits from coordination among multiple agents. The value comes less from the number of agents involved and more from how effectively their capabilities are combined. Depending on the use case, swarms can improve speed, capacity, flexibility or the quality of outputs. For some workflows, a single agent can remain the simpler and more effective approach.

• Adaptability: A swarm can adjust as new information emerges. Agents can respond to changing conditions, revise plans or redirect work without requiring every step to be predefined. This is useful when new information can change what needs to happen next or when the work is difficult to predict upfront.
  
  
• Delegated execution: Routine portions of a workflow can proceed without a person directing every individual step, provided the agents have clearly defined permissions and coordination rules. Human review can remain mandatory for sensitive or consequential actions.
  
  
• Distributed workload: Distributing work across multiple agents can help organizations handle larger volumes of work, making it easier to support high-volume or resource-intensive workflows. This benefit is relevant when independent work can be spread across multiple agents, including through horizontal scaling.
  
  
• Multiple perspectives: Independent agents can approach the same problem from different angles, generating alternative ideas, recommendations or analyses. This can help teams when comparing perspectives, exploring options, challenging assumptions and identifying issues that deserve further investigation. In some workflows, combining these contributions can create a form of collective intelligence by drawing on multiple independent responses.
  
  
• Faster execution: Parallel work may reduce elapsed time by allowing independent tasks to run at the same time.
  
  
• Improved quality control: Having agents examine the same problem or review each other’s outputs can surface disagreements and possible errors. This is most useful when those discrepancies would be difficult to identify through a single pass, but it does not guarantee correctness, particularly when agents share the same model, data or assumptions.
  
  
• Specialized capabilities: Different parts of a workflow can use capabilities suited to their specific requirements. This is helpful when one process needs a combination of models, tools or execution environments, such as Anthropic’s Claude models and coding tools such as Claude Code.
  
  
• Workflow flexibility: A workflow can be changed by modifying individual components rather than redesigning the entire process. This modularity can be valuable when requirements, tools or models are likely to change over time.

Agent swarms can fail in several ways as work moves between agents. An agent might produce an incorrect or incomplete result. Another agent might build on that result, or different agents might reach conflicting conclusions.

Problems also arise when agents receive the wrong context, lose information between steps or take unintended actions. These failures can make swarm behavior harder to troubleshoot and control.

• Conflicting outputs: Independent agents can reach different conclusions or produce incompatible results. The swarm needs a way to compare outputs and decide how disagreements should be handled.
  
  
• Context management: As workflows become more complex, deciding what information each agent should receive can become a challenge.
  
  
• Coordination overhead: The system needs to manage task assignments, information sharing and conflicts between agents. Coordination can become more difficult as workflows involve more agents and interactions.
  
  
• Error propagation: An error from one agent can become an input for another. Without appropriate checks, a mistake can move through the workflow and affect the end result.
  
  
• Higher costs: Running multiple agents can increase model and computing costs, particularly when agents exchange large amounts of information, interact frequently or repeat work.
  
  
• More difficult debugging: When a swarm produces an incorrect result, the source can be an individual agent, an interaction between agents or the way information was passed through the workflow. This can make problems harder to trace than in a single-agent system.
  
  
• Security and access: Different agents might need access to different data, tools or systems. Those permissions need to be carefully controlled, particularly when agents can act without direct human instruction.
  
  
• Unnecessary complexity: Multiple agents do not provide benefit for every task. For simpler problems, the extra coordination and infrastructure can add complexity without providing enough value to justify it.

An AI agent swarm can be useful when dividing a task among multiple agents provides a clear advantage over handling it with a single agent or another approach. For a more complex implementation, a proof of concept (POC) can help determine whether the benefits justify the added coordination, resources and complexity before a swarm is more broadly deployed. Consider an agent swarm when:

• The problem can be divided into meaningful components: Different parts of the work require separate activities, decisions or areas of expertise.
  
  
• Parts of the work can happen in parallel: This approach can reduce elapsed time, but the tasks need to be genuinely independent and the time saved needs to outweigh the coordination required.
  
  
• No single agent is well-suited to the entire task: The workflow benefits from combining different capabilities, tools, models or instructions. Separate agents make more sense when the goal is to give each part of the workflow capabilities tailored to its specific requirements.
  
  
• The scale of the work exceeds what one agent can efficiently manage: The volume or complexity of the work is substantial enough to divide among multiple agents. Distribution becomes more relevant when a single agent would struggle to handle large, ongoing or resource-intensive workloads.
  
  
• The workflow needs to adapt as conditions change: Agents may need to make decisions, respond to new information or dynamically redirect work as a process unfolds. One agent can examine another agent’s output, or multiple agents can solve the same problem independently. This approach is most relevant when the full process is difficult to predict or is vulnerable to changing external conditions. It also allows agents to compare results, which can reveal disagreements or possible errors that would otherwise go unnoticed.
  
  
• Coordination itself creates value: The outcome depends not only on completing tasks, but on combining contributions from multiple agents—even multiple teams—to produce more useful results.

A swarm might not be appropriate when the task is straightforward, when each step depends heavily on the previous one or when the addition coordination of multiple agents would provide little benefit. In those situations, a single agent or conventional automated workflow can be simpler, easier to govern and less costly to operate.