Single Agent vs Multi Agent in AI: Choosing the Right Intelligence Architecture

TL;DR

• Single-agent AI systems are simpler, faster to build, and ideal for tightly scoped tasks (like summarising emails or handling specific customer queries through chatbots). However, they lack scalability, specialisation, and resilience.
• Multi-agent AI systems break tasks into subtasks handled by specialised agents under an orchestrator, enabling modularity, fault tolerance, and better scalability, ideal for complex, multi-domain workflows, like an autonomous customer support system, an AI research assistant, etc.
• Single-agent AI: low cost, easy debugging, and unified context. However, limited to linear workflows, and failure-prone.
• Multi-agent AI: resilience, task optimisation, and better resource use. However, require orchestration, context sharing, and observability.
• Choose single-agent for rapid prototypes and narrow tasks; opt for multi-agent when the use case demands domain specialisation, cross-functional coordination, and future scalability.

We’re now in the mid-2020s, and productivity has become a major driver of efficient workflows and business growth. A recent global study by IBM, which surveyed 2,900 executives, highlights the rising adoption of AI agents in organisations. The study found that AI-enabled workflows are expected to increase from 3% today to 25% by the end of 2025. Moreover, 70% of executives believe that agentic AI will play a very significant role in their organisation’s future. So, the interest around AI agents is growing very fast. But the real question is, what type of setup do you need with AI agents? A single-agent setup or a multi-agent one?

In this article, we’ll explore single agent vs. multi-agent in AI to understand the differences in their core capabilities and ideal applications. Let’s dive in.

Comparing Single-Agent vs. Multi-Agent in AI

The main point of comparing Single-Agent vs Multi-Agent in AI isn't just the technical differences between the two systems. It’s very obvious that a multi-agent AI system can handle far more complex tasks than a single-agent AI. But do you actually have a task that complex to begin with? A single-agent AI system is much easier to deploy. You can literally build one in a few hours. However, as tasks grow in complexity, a single-agent system may no longer be sufficient.

There are also several other factors to consider. The table below provides a comprehensive comparison of complexity, development speed, efficiency, scalability, and other significant aspects between single agent and multi agent AI systems.

Single Agent vs Multi Agent in AI

Single-Agent AI Systems

What is Single Agent Architecture?

T-Mobile Austria's "Tinka" chatbot, a single-agent AI system, has been in operation since 2015 and can provide answers to more than 1,500 questions that customers commonly ask. If it does not have an answer to a user's query, it has a feature called LiveAgent Handover, which redirects the user to a human agent. The single-agent system involves one intelligent agent that handles the entire task lifecycle, ingesting inputs, reasoning, tool use, and output generation. It connects to APIs or databases via protocols like the Model Context Protocol or RESTful services and manages its own memory and state.

Pros (Single-agent AI)

• In single-agent AI, only one agent is involved in decision-making. So, the decision-making procedure is faster here. It is just like a sole proprietorship, where only one person is involved in the business and he/she is making all the decisions for himself/herself. The procedure would have been slower if it were a company where a board meeting needed to be called for the decision-making procedure.
• Single-agent AI is easier to develop. It may not require a complex infrastructure or hefty computing resources to build. You can use a no-code platform like Botpress, define the agent's purpose, set up rules or workflows, and connect it to various tools or data sources. Just like that, you'll have your very own single-agent AI. The platform handles the underlying AI complexities.

Cons (Single-agent AI)

• In a sole proprietorship, as we have illustrated in the example above, if the person in charge becomes ill, there is no one else to run the business. Similarly, a single-agent AI system has a single point of failure. If the AI agent encounters a technical error or fails to operate for any reason, the entire system stops functioning.
• It won’t be very feasible to execute a complex task with a single-agent AI. In fact, if you give it a multi-faceted or complex problem to solve, the performance may significantly diminish. In that case, we’ll need multiple agents to solve the problem.

Real-World Example of Single-Agent AI: Slackbot (Helpdesk Automation)

Many startups or enterprises deploy Slack-based LLM agents (such as OpenAI-integrated bots) to handle internal helpdesk queries. These bots ingest employee queries (e.g., “How do I reset my VPN?”), search a knowledge base, and respond, all within a single-agent architecture. They’re fast to implement and highly efficient for single-domain questions, but struggle when expanding to more complex HR or legal queries that require multiple skill sets.

Multi Agent AI Systems

What is Multi-Agent Architecture?

We have understood what a single-agent AI system is. Now let’s move to the concept of a multi-agent AI system. Unilever is a company that operates in over 190 countries across the world. Due to the large number of résumés they receive for a single job role, the hiring process becomes very difficult, especially when it comes to identifying top talent among so many applicants. So, how did they solve the problem?

Unilever partnered with an AI-powered recruitment expert called Pymetrics (now acquired by Harver). Pymetrics has a multi agent AI setup in which several autonomous AI agents collaborate to evaluate candidates and match them with suitable job roles. Together, they created an online hiring platform. On this platform, the AI system screened candidates using games and video interviews, analysing logic, risk appetite, language, and body language. This automated process matched applicants with profiles of previously successful employees. According to Leena Nair, former Chief Human Resources Officer at Unilever, this AI system has saved around 70,000 hours of human assessment time.

This is how important multi-agent AI systems have become in today’s world. A multi-agent system (MAS) consists of more than one artificial intelligence (AI) agent working in collaboration to perform complex tasks, often on behalf of an enterprise, another system, or simply for a user.

Pros (Multi-agent AI)

• A multi-agent setup can handle significantly more complex tasks compared to a single-agent AI. In fact, multi-agent AI can often perform the work of a single-agent AI with greater efficiency or effectiveness. In a research done by Anthropic, a multi-agent system, having a lead agent, which was Claude Opus 4 in their experiment, and Claude Sonnet 4 subagents, performed 90.2% better than a standalone Claude Opus 4 model in internal evaluations.
• A multi-agent AI system has the ability to adapt in real time to evolving situations and unexpected issues. For example, consider a customer support platform that is powered by multiple AI agents. If there's a sudden spike in support tickets during a product outage, one agent can prioritise urgent cases, another can fetch relevant technical documentation, and a third can interact directly with users. This coordinated response helps the system handle the crisis efficiently. It is something a single-agent setup may fail to manage under pressure.
• If one agent encounters an issue, the remaining agents can still operate and delegate the work accordingly among themselves. It prevents the entire system from shutting down.

Cons (Multi-agent AI)

• Developing a multi-agent system is like organizing a team of specialists who must constantly communicate and collaborate. If you need to manage a large project with many team members, it will require briefing clear roles to each member, coordination tools, and efficient communication. Just like that, multi-agent AI demands advanced algorithms to ensure agents work together smoothly without conflict.
• Also, a multi-agent AI system will require robust computational knowledge. It will need orchestration platforms, task queues (like Celery or LangGraph), and persistent memory layers (Redis, VectorDBs). Therefore, it may not be as simple to set up as a single-agent system.

Real-World Example of Multi Agent AI: AutoGPT and CrewAI

Open-source frameworks like AutoGPT and CrewAI illustrate multi-agent design. In AutoGPT, a planning agent decomposes a task (e.g., “Build a website”) into subtasks like gathering content, coding HTML, and testing, assigning each to a specialised agent.

In CrewAI, you define roles like Researcher, Analyst, and Writer. When asked to “summarise quarterly financial reports,” the Researcher fetches data, the Analyst evaluates trends, and the Writer drafts a summary. The orchestrator oversees the flow, providing fault tolerance and specialisation at scale.

Enterprise Example for Multi Agent AI: Shopify’s Sidekick

Shopify’s Sidekick uses a multi-agent backend where customer-facing AI agents handle user queries, while background agents fetch data from product listings, inventory databases, and order histories. The response generation agent combines the outputs, ensuring conversational fluidity while integrating real-time business data.

Single Agent vs. Multi Agent in AI: Design Patterns

There are some basic differences in the design patterns of single agent and multi agent AI. In the case of a single agent system, you can make a basic structure like a pipeline chaining, or ReAct agent. However, in the case of multi-agent AI, the design patterns can be quite complex and even diversified. Below you will find some design patterns that are used to build AI agents. The first two are suitable for single agent AI and the rest patterns are used to build multi agent AI system.

Prompt or Pipeline Chaining (Single or Lightweight Multi-Agent)

This pattern is often used in single-agent systems or lightweight agent chains. Tasks are executed sequentially, with each stage’s output becoming the input for the next step. While this can be managed by a single agent prompting itself iteratively, it’s also used in minimal multi-agent setups where agents are tightly coupled.

• Used In: LangChain’s SequentialChain, Robocorp LLM Email Assistant
• Benefits: Simpler than full orchestration; effective for structured, linear workflows.
• Example: A single GPT-4 agent summarises an email, generates a draft reply, and sends it, all in one context.

ReAct Agent (Single Agent Pattern)

The ReAct (Reasoning and Acting) pattern allows a single AI agent to alternate between reasoning steps (via an LLM) and actions (using tools like Google or file systems). It is widely used in LLM agents to perform tool-augmented thinking.

• Used by: Most AI agent products including LangChain-based agents.
• Benefits: Combines reasoning and tool use in a single loop, enabling dynamic decision-making and problem-solving. Enhances task flexibility, supports real-time tool use, and is ideal for agents requiring step-by-step reasoning with external interactions.
• Example: A support bot that interprets a user query, searches Google, and returns a formatted answer.

Orchestrator/Gatekeeper (Multi-Agent)

This is a core design pattern in multi-agent systems. A central orchestrator or gatekeeper oversees task delegation, error handling, and flow control. It receives a user prompt or task and intelligently routes subtasks to specialised agents, such as a data-fetching agent, analysis agent, or content-generation agent.

• Used In: CrewAI, LangGraph, MetaGPT
• Benefits: Modular and scalable; allows separation of concerns and easier fault recovery.
• Example: In Shopify's Sidekick, the orchestrator routes user queries to agents that handle sales data, discount configuration, and communication setup.

CodeAct Agent Pattern (Multi-Agent)

The CodeAct pattern allows agents to autonomously execute Python code instead of just passing JSON outputs. This enhances flexibility for dynamic or iterative problem-solving.

• Used by: Manus AI
• Benefits: Offers high flexibility by allowing agents to write and execute Python code autonomously, enabling more complex, dynamic problem-solving; reduces the need for external orchestration and allows for real-time feedback, iteration, and adaptation.
• Example: A data science assistant that writes and tests Python code blocks autonomously to build visualisations or automate reporting.

Agent-to-Agent Protocol (A2A) (Multi-Agent)

This pattern enables direct communication between multiple agents using a shared schema or messaging protocol. It is fundamental to scalable, decentralized multi-agent systems. Each agent can independently understand and respond to requests from others, allowing flexible, asynchronous coordination.

• Used In: OpenAgents, MetaGPT, and emerging open multi-agent platforms
• Benefits: Decentralisation, task parallelism, and resilience; agents can operate independently and still cooperate effectively.
• Example: In a financial reporting system, a data agent pulls quarterly metrics, a summariser agent condenses them, and a compliance agent validates the content before sending it—all through structured inter-agent messaging.

Self-Reflection Agent Pattern (Multi-agent)

This pattern involves agents that critique or evaluate their own outputs. The main LLM generates a first draft and sends it to a critic LLM for evaluation. If needed, the system rewrites the answer before returning it.

• Used by: OpenServ
• Benefits: Improve accuracy and coherence by iteratively evaluating their own outputs. Enhances reliability, reduces hallucinations, and produces higher-quality responses, especially useful in long-form content or critical decision-support scenarios.
• Example: An AI writing assistant that writes an article, critiques its tone or accuracy, and rewrites it based on internal feedback.

Agentic RAG (Multi-Agent)

This pattern enables agents to retrieve data from external sources (like vector databases) and combine it with memory/context before generating output.

• Used by: Perplexity
• Benefits: Boosts knowledge depth by retrieving and grounding responses in external data sources like vector databases. Enhances factual accuracy, reduces hallucinations, and provides richer, context-aware outputs for research-heavy or data-intensive tasks.
• Example: A research assistant that queries a vector DB of scientific papers, pulls relevant excerpts, and synthesises a summary with citations.

Modern Tool Use via MCP (Model Context Protocol)

Some agents, like those built by Cursor, use Model Context Protocol to interface with modern tools like Brave Search or AWS services. These toolchains don’t require full code execution and operate efficiently within agent systems.

• Used by: Cursor
• Benefits: Enables seamless integration with modern tools like AWS or Brave Search without executing code. Improves security, speeds up response time, and simplifies tool chaining in agent workflows.
• Example: An agent that pulls cloud usage data from AWS and enriches it with live web search to generate real-time reports.

Single Agent vs Multi Agent AI: When to Choose Which

Choose Single-Agent If:

• You’re building a rapid prototype or MVP.
• The task is narrow in scope, such as internal FAQ bots, document summarisation, or ticket triage.
• Your team lacks the resources or expertise for orchestration complexity.

Example: The open-source Robocorp LLM Email Assistant uses GPT‑4 to automatically summarise incoming emails, extract structured data, and generate suggested replies. It’s used, for example, to process high‑volume invoice‑related email threads, capturing context, flagging outstanding invoices, and composing follow‑ups via SendGrid.

Choose Multi-Agent If:

• The task involves multiple domains or requires diverse data sources.
• You need reliability and isolation (e.g., regulatory reporting, multi-lingual content generation).
• Scalability and future feature expansion are critical.

Example: BNY Mellon deploys a multi-agent AI system where specialised agents autonomously handle financial workflows such as onboarding, compliance, and communication. Each agent performs domain-specific tasks like document parsing or knowledge retrieval, coordinated under human oversight. This architecture enhances modularity, efficiency, and scalability across regulated enterprise operations.

Conclusion

The debate between single agent vs multi agent in AI hinges on one key question: what is the problem you're solving, and how complex is it?

A single-agent architecture suits lightweight, fast-deployment scenarios where simplicity, speed, and scope are tightly controlled. Think of it as the agile sprinter, quick and effective for the short game.

A multi-agent system, in contrast, behaves like a relay team; coordinated, powerful, and adaptable for more complex, long-haul workflows. It excels in modularity, fault tolerance, and scalability, making it ideal for mature or enterprise-grade automation.

Developers can now build agent ecosystems tailored to their goals with frameworks such as AutoGPT, LangChain, CrewAI, and enterprise platforms such as SAP Joule and Salesforce Einstein GPT. By understanding the strengths and trade-offs of each architecture, system architects and AI engineers can deploy solutions that are robust, adaptable, and future-ready.

FAQs

1. What is the difference between single agent and multi-agent?

The key difference in the Single Agent vs Multi Agent in AI debate lies in architecture and scalability. A single agent is one AI entity that handles all tasks independently, ideal for simple, linear workflows. In contrast, a multi agent AI system uses multiple specialised agents that collaborate to complete complex tasks. Each agent focuses on a specific responsibility, making the system more modular, scalable, and resilient.

2. What is the difference between multi-agent and multi-model?

Multi-agent AI refers to systems with several autonomous agents working together, each handling a specific part of a task. Multi-model, on the other hand, involves using different types of AI models (e.g., image models, text models) within the same workflow. While both involve task distribution, multi-agent systems focus on coordination between agents, whereas multi-model setups focus on combining different AI capabilities.

3. What is an example of a single agent and multi-agent environment?

A good example for single agent environment is an AI email assistant that reads, summarises, and replies to emails on its own. A multi-agent environment would be something like an AI system in e-commerce, where one agent handles product recommendations, another processes payments, and a third manages customer queries. These reflect the practical divide in single agent vs multi agent systems.

4. What is the difference between multi-agent systems and distributed systems?

While both deal with multiple components, multi agent AI systems consist of autonomous agents that make decisions independently or collaboratively. Distributed systems, in contrast, are designed for load distribution and parallel processing but don't necessarily involve intelligent decision-making. In short, multi-agent systems focus on intelligence and autonomy, while distributed systems focus on computational efficiency.

5. What is Agent in AI?

An agent in AI is a software component that observes its environment, interprets goals, and acts to achieve them, often autonomously. It can use LLMs, fine-tuned models, or hybrid tool integrations. These agents are key to building intelligent assistants, self-operating bots, and end-to-end automation pipelines. If you’ve ever wondered what is agent in AI, it refers to this self-directed system that forms the backbone of intelligent assistants, autonomous bots, and full-scale automation pipelines.