A practical guide to how AI agents coordinate, what separates the three dominant architecture patterns, and when a multi-agent approach beats a single capable agent.
Agentic systems are sold by many vendors, and companies are beginning to move their workflows from pilots into production. While Gartner reports that only 17% of organizations have deployed AI agents, more than 60% expect to do so within the next two years. Moreover, Gartner also recognizes multi-agent systems as a top strategic priority. This article covers what multi-agent systems are, how the three dominant architectures compare, and when a multi-agent system is the best approach for your workflows.
Key takeaways
- Multi-agent systems distribute specialized work across multiple AI ‘worker’ agents that are coordinated by an ‘orchestrator’ agent.
- Both the Model Context Protocol and Agent-to-Agent protocols standardize how agents connect to tools and communicate with each other, reducing the need for expensive, custom coding.
- The most common challenges include underspecified orchestration, coordination overhead that exceeds the efficiency gained, or a lack of observability.
What is a multi-agent system?
A multi-agent system (MAS) is a framework of multiple autonomous AI agents that coordinate within a shared environment to accomplish a goal beyond the scope of what any single agent can handle well on its own. Each agent within a MAS is assigned a specialized role, tool access, and capabilities or limitations. In practice, this often means a central orchestrator agent directs several worker agents through a standard process where each agent handles only specific tasks and then hands off the work to the next agent. It’s the software equivalent of a cross-functional team, managed by a project manager to help coordinate tasks across a workflow.
How multi-agent systems work
Regardless of an agent’s role within a MAS, each agent runs through the same essential loop:
- Perception: the agent observes its environment and gathers the data it needs.
- Reasoning: its underlying LLM processes that input and decides what to do next.
- Action: the agent executes the task within its defined scope.
- Interaction: the agent passes results and context back through the orchestrator agent.
There are different ways that a MAS can function, but in a hub-and-spoke system, the orchestrator breaks down the end goal into subtasks, routes each task to the right worker, and then aggregates the results. This means that agents are stateless relative to the full workflow: each receives a defined input, does its job, and returns a structured output. Agents do not learn from one another or gather additional context, so the orchestrator role requires careful design — in fact, the orchestrator agent is the single most important architectural decision in any multi-agent system.
Multi-agent system architecture types
These are typically the primary architecture types represented in production, each with its own trade-offs.
1. Hub-and-spoke (orchestrator-worker)
One central orchestrator directs every worker agent. This is the most common pattern because it's the easiest to debug and best suited to independent subtasks, as the orchestrator can catch and contain errors before they spread.
2. Flat mesh (peer-to-peer)
Agents communicate directly with each other with no central coordinator. This offers the highest fault tolerance (meaning the process can proceed even if interrupted or a task fails), but it can be difficult to debug if something goes wrong and is not ideal for repeatable business workflows.
3. Hierarchical
Represented by a tree structure with manager, specialist, and worker tiers. This can work well for enterprise workflows that need specialized domain expertise at more than one layer. For example, where a site-level manager agent coordinates several specialist agents beneath it.
Where to begin?
Generally, you can default to a hub-and-spoke model unless you know you have a specific requirement (true fault isolation or independent peer agents). For most non-technical teams managing general business workflows, mesh or hierarchical designs will not apply.
The orchestrator agent: the brain of the system
The orchestrator handles task decomposition, routing, state management, and error recovery. This means the orchestrator breaks down big goals, assigns tasks, and fixes routing problems, but doesn’t do any of the actual work. Expert domain work, like coding or data analysis, stays with the workers. Put another way: the orchestrator is the project manager, and the workers are the specialists. Keeping that separation clean is what makes the system reliable.
When things fail, it’s generally not because of a weakness in individual worker agents. The most common failure mode is typically that the orchestrator’s task-decomposition logic is underspecified. Too often, teams spend time tuning worker agents, but if the decomposition is bad, the orchestrator can break the entire pipeline no matter how good the individual workers are.
How MCP and A2A protocols enable agent coordination
Anthropic introduced the Model Context Protocol (MCP) in November 2024 to standardize how AI agents connect to tools, using JSON-RPC 2.0 and session IDs to preserve context across handoffs. In December 2025, Anthropic, OpenAI, Microsoft, and Google jointly formed the Agentic AI Foundation under the Linux Foundation and contributed MCP (alongside OpenAI's AGENTS.md) as an open, cross-vendor standard.
Google's Agent-to-Agent protocol (A2A) does the equivalent job for agent-to-agent communication, standardizing how an orchestrator hands work to a worker regardless of which framework built it. Together, MCP and A2A function as a standardization layer, allowing teams to upgrade or swap individual agents without rewriting the whole coordination layer underneath them.
Single-agent vs. multi-agent systems: when to use each
You’ll need a multi-agent system when:
- The task decomposes cleanly into independent subtasks by domain.
- Parallel processing genuinely outweighs coordination overhead.
- A single context window is too small for the full task.
- You need a dedicated validator or critic agent, or fault isolation matters.
Stick with a single agent when:
- The task requires tight sequential reasoning. Google Research found that multi-agent coordination degraded performance by 39–70% in these cases (even as it improved performance by roughly 81% on parallelizable work like financial analysis).
- The work needs fewer than 10-15 tool calls from a single domain.
- Your team doesn't yet have observability infrastructure in place.
- The real problem is prompt engineering, not architecture.
According to Arion Research’s 2025 year-end review of agentic AI deployments, the most successful multi-agent systems kept teams limited to 3-5 agents. Deployments that launched with 20 or more agents consistently underperformed. Complex coordination overhead can outpace gains.
Challenges of building multi-agent systems
Like anything else you build, it takes time and thoughtful design to ensure a successful outcome. While AI promises to do everything faster, the way your system is architected makes all the difference. Here’s a look at some common challenges.
Coordination overhead
Latency compounds with every handoff — design for latency from the beginning, rather than discovering it in production.
Observability
Agent observability is critical. Debugging ten agents without distributed tracing becomes difficult and is a support problem waiting to happen.
Prompt injection across agent boundaries
User-supplied data passed between agents can override a worker's system prompt if it isn't sanitized at each handoff.
State management
Shared versus distributed state is a real trade-off between speed or scale, so be sure to think through this.
Why systems fail and how to mitigate
When a multi-agent system breaks down, the cause is almost never the model — instead, the issue likely lurks in the architecture and coordination layer, whether it’s an underspecified orchestrator, coordination overhead that exceeds the efficiency gained, or a lack of observability.
There are a few practical ways to avoid points of failure:
- Invest in orchestrator design
- Keep initial rollouts to 3-5 agents
- Use structured JSON output at every handoff
- Build tracing before deploying to production
- Design escalation paths at the orchestrator level
- Evaluate full pipeline performance (not just individual agents)
Coordinate your multi-agent workflows with Airtable
Airtable gives teams the structured system of record that multi-agent systems need to stay reliable: a place to track the state of every task an agent picks up, log the inputs and outputs each worker produces, and coordinate the human review and approval steps that every MAS still requires. Airtable is the coordination layer where agents and the people overseeing them work from the same context, across the same operational surface.
Depending on your needs and use case, there are a few ways this comes to life. Omni lets users build apps and tables using natural language, turning a workflow into something Field Agents (specialized worker roles) can execute at scale inside records, whether that’s enriching records, analyzing documents, or generating content. Developers can also connect agents built using other frameworks, like LangGraph, to Airtable via APIs or custom webhooks to automate and orchestrate your cross-functional workflows.
Build your first workflow
Frequently asked questions
An AI assistant responds to queries — it generates text, summarizes content, or answers questions (like a chatbot than answers FAQs), but doesn't take action in external systems. An AI agent can perceive its environment, make decisions, call tools, update records, and execute multi-step workflows autonomously. This might look like reading data in your CRM, scoring a lead, routing it to a rep, and logging the outcome.
Best-practice deployments start with no more than 3-5 agents. Teams that launched with 10 or more agents consistently underperformed because coordination overhead consumed the efficiency gains the architecture was meant to create. Scale agent count only when performance data justifies the additional complexity.
The most widely adopted frameworks in 2026 are LangChain (the foundational open-source framework for building LLM applications and agents) and LangGraph (built on top of LangChain, adding graph-based stateful workflows), CrewAI (role-based agent teams), AutoGen from Microsoft, and the OpenAI Agents SDK.
Not typically for the full build, but the gap is narrowing. No-code platforms like Hyperagent (https://www.hyperagent.com/), Relevance AI, Lindy, and Metaflow AI expose multi-agent orchestration through visual builders that non-technical teams can configure. For production enterprise deployments using frameworks like LangGraph or AutoGen, engineering involvement is generally required for the integration and monitoring layers, even if the workflow logic is designed by a business user.
The key signals are: task completion rate (what percentage of workflows reach a successful end state without human intervention), error rate and error type (misrouting, hallucinated outputs, tool failures), latency per task delegation cycle (should be consistent; spikes indicate orchestrator overload), and end-to-end pipeline evaluation scores (individual agent unit tests are insufficient — test the full workflow). Distributed tracing tools are essential for root-cause analysis when pipelines fail.
When multi-agent systems fail, the cause is almost never the model — it's the architecture and coordination. Common failure modes: underspecified orchestrator, coordination overhead exceeding efficiency gains, communication overload, and lack of observability. The fix is to invest in your architecture: focus on orchestrator design, keep initial rollouts small, and build in tracing before agents are in production.
No, ChatGPT is a single model (single-agent system). A multi-agent system involves multiple autonomous agents coordinating. You can build multi-agent systems on top of GPT using frameworks like AutoGen or CrewAI.
