Single Agent vs Multi Agent: When to Use Multi-Agent Systems in AI

Omega began as an internal R&D project—a lightweight Slack tool designed to help our sales team with day-to-day tasks. It could retrieve documents, summarize call notes, and surface relevant links on request. Powered by a single language model, it was reactive, helpful, and operated within a fixed prompt structure. This single agent setup was simple and fast, making it ideal for specialized tasks such as data analysis or customer support. Such systems excel at handling straightforward, narrowly defined applications efficiently. As we added new tasks, we expected more: Not just answers, but initiative. Not just assistance, but reasoning.

We wanted Omega to generate agendas based on context. To proactively suggest features for proposals. To review its own outputs and improve them. That’s when it stopped being just an assistant. It became an AI agent—an autonomous component capable of deciding what tools to use, when to ask follow-up questions, and how to structure its work based on goals.

But even then, limitations appeared. One agent, no matter how well prompted, struggled with multi-step workflows. It lacked the ability to critique itself, to break down tasks into subcomponents, or to delegate responsibilities effectively.

So we introduced a second agent. Then a third. Each with a specific role. They began to collaborate: one writing, one reviewing, another planning—using different agents for distinct responsibilities. Omega evolved into a multi-agent system—and that shift unlocked a new level of reliability, clarity, and performance.

Perhaps the difference between a single agent and a multi-agent approach is key to understanding which architecture best fits your needs. That shift raised important questions—not just about what we were building, but how others might face similar decisions as their AI projects grow in complexity.

• When is a single agent enough?
• When do you need a team of agents?
• And how do you scale without losing control?

In this article, we’ll share what we learned while building our AI agent—from the evolution of assistant to agent to multi-agent system.

What Are Multi-Agent Systems in AI?

A multi-agent system is an architecture where multiple autonomous agents—often built with large language models—collaborate to complete a task. The underlying multi agent architecture enables these agents to work together efficiently, facilitating communication and coordination. A multi agent framework serves as an integrated system that manages and coordinates the roles and interactions of the agents. Each agent in the system performs a specific role, such as planning, writing, validating, or retrieving data, with multiple specialized agents working together to achieve complex objectives. These agents are examples of intelligent systems, capable of autonomous decision-making and adapting to specific tasks or environments.

In contrast to a single-agent approach, a multi-agent LLM system distributes the load, allowing more specialized reasoning and structured workflows. These systems are a significant development within the broader field of artificial intelligence, enabling advanced collaboration and problem-solving capabilities.

Understanding Solo Agents vs. Multi-Agent Systems in AI

When we talk about AI agents in this article, we mean systems that can make decisions, trigger actions, and complete tasks with a degree of autonomy. This falls under the broader concept of agentic AI, which includes both single-agent and multi-agent workflows. Unlike traditional AI assistants, which typically respond to single prompts or direct commands, agents interpret intent, use external tools, and act based on predefined goals—not just user instructions. Agentic systems provide frameworks for dynamic, natural language-based actions, enabling large language models (LLMs) to interact with structured systems and enhance overall functionality.

There are two ways an AI agent can operate in practice:

Solo AI Agent

A solo AI agent handles an entire task from start to finish. It plans, executes, and responds using its own logic or prompt instructions—without delegating to other agents. Single agents provide speed and efficiency for straightforward, specialized tasks, making them ideal for rapid deployment and simple workflows. This setup typically relies on a single agent system, which is well-suited for well-defined, self-contained tasks: summarizing a document, drafting a quick reply, or retrieving specific information from a known source.

We used this approach in Omega’s early development. The agent received a request in Slack, gathered relevant context, and responded—all within a single flow. In deployment, individual agents are often preferred when simplicity, efficiency, and clear boundaries are required, especially in less complex environments.

Multi-Agent System

A multi-agent system involves multiple AI agents working together toward the same goal. Multiple agents collaborate by dividing responsibilities, sharing information, and synchronizing their actions to achieve shared objectives. Each agent has a specific role—like generating content, reviewing outputs, or gathering supporting data. They pass context between each other, collaborate on intermediate steps, and coordinate actions to handle more complex workflows. Coordination logic is essential for managing these agent interactions, ensuring reliable communication and state synchronization.

This structure helped us evolve Omega. Instead of expecting one agent to write, check, and format a proposal, we introduced dedicated agents for each of those tasks—resulting in clearer logic, better outputs, and easier debugging. Workflow orchestration is a key component in multi-agent systems, providing structured strategies for managing agent communication and seamless integration.

Choosing between a solo or multi-agent setup isn’t just about scale—it’s about how the task is structured, how much reasoning it requires, and how predictable the flow needs to be. For complex tasks, a multi agent workflow offers significant advantages in efficiency and results.

When Solo AI Agents Fall Short

Solo agents are often a great starting point. They’re faster to build, easier to manage, and well-suited to many everyday tasks. But as the complexity of the problem increases—more steps, more context, more decisions—they begin to show limits. When facing complex challenges that exceed the capabilities of solo agents, a more advanced approach is needed.

We saw this with Omega. As long as the task involved retrieving a file or summarizing a single document, the system performed well. But once we asked it to generate multi-step agendas, track context across tools, or review its own outputs, the performance dropped. Responses became inconsistent, and logic started to break down, highlighting the importance of robust error handling to manage failures and maintain reliability.

Why? Because we were pushing one agent to do everything: reason, fetch, generate, format, critique—and do it all in a single loop. Additionally, the context window of a single agent limits how much relevant information it can process at once, which impacts the quality of outputs for tasks involving extensive data or lengthy texts.

That’s when we introduced a multi-agent system setup.

Here’s how the two approaches compare:

Multi-Agent System

Capability / Factor	Solo Agent	Multi-Agent System
Task Simplicity	Ideal for simple, well-scoped tasks	Better suited to layered or ambiguous tasks
Context Handling	Limited by token size and prompt logic	Context can be split across agents
Reasoning Steps	One-pass, sequential logic	Multi-step reasoning, handled in stages
Error Detection / Review	No internal critique unless scripted	One agent can validate or improve another
Development Speed	Fast to prototype	Requires coordination and orchestration
Debugging	Easier to trace problems	More complex due to distributed logic
Scalability of Roles	Harder to modularize	Easy to extend with additional agent roles; can modularize the entire system for better management
Cost & Latency	Lower inference cost and response time	Potentially higher due to multiple passes

Multi-agent systems can modularize the entire system, making it easier to manage complexity, security, and scalability as requirements grow. While solo agents are often used in more controlled systems for predictable outcomes, multi-agent architectures offer greater flexibility but may introduce new challenges in coordination. In both single agent and multi-agent setups, maintaining data integrity is essential to ensure reliable and accurate outputs, especially when handling ambiguous or unstructured data.

Common Signs a Solo Agent Is Hitting Its Limits

• Outputs become vague or incomplete when handling multi-step tasks.
• Prompt tuning reaches a ceiling in terms of effectiveness, highlighting the need for advanced prompt engineering to further optimize agent performance.
• Logic errors or hallucinations increase with context size, and the lack of long term memory limits the agent's ability to handle complex tasks that require retaining information over extended periods.
• The agent’s response requires constant human follow-up or correction.

Solo agents work best when the task can be resolved in one logical pass. But as soon as the task needs multiple passes, roles, or specialized behaviors, it’s worth considering a multi-agent structure—not for complexity’s sake, but for clarity, maintainability, and better outcomes.

In the next section, we’ll look at specific use cases where multi-agent collaboration provides a measurable benefit.

When to Use a Multi-Agent System

Multi-agent systems aren’t always needed—but a key part of the decision-making process is determining how many agents are required for a given task. They become valuable when a task exceeds what a single agent can handle reliably. When transitioning to multi-agent AI systems, it's important to build systems that can scale and adapt to increasing complexity. Based on our experience, here are the most common scenarios where switching to a multi-agent setup makes sense:

Task Complexity

The task involves multiple distinct steps that require different types of processing. For example: understanding a brief, generating a proposal, and then validating it. Each step benefits from a focused, specialized approach. In multi-agent systems, specialized agents are assigned to handle these distinct steps, allowing each agent to leverage its expertise for specific tasks within the workflow.

Sequential Logic

Some workflows must follow a strict order—step B depends on step A. Breaking the flow into agents that hand off results in sequence (e.g., summarize → extract → format) improves structure and traceability.

Parallel Execution

When speed is a factor, certain subtasks can run in parallel. Multi-agent systems can fetch documents, generate summaries, and analyze inputs at the same time—reducing latency compared to one agent doing it all.

Specialization by Role

Just as human teams perform better with defined roles, agents can be tuned or instructed to focus on one task—writing, reviewing, planning, searching. This avoids prompt overload and keeps logic clean. When multiple specialized agents collaborate, they can divide complex tasks and communicate to achieve more efficient and effective results than a single agent alone. For example, content creation can benefit from agent specialization, with different agents handling ad copy, branding assets, and marketing insights.

Error Resilience and Feedback Loops

One agent might generate an answer, but another can review it for logic, tone, or completeness. These internal feedback loops help catch issues early and improve the system’s reliability over time. Effective error handling within these loops is crucial for maintaining system robustness and ensuring that failures or exceptions do not compromise performance. After agent collaboration and review, the system produces a unified final response that reflects a well-reasoned conclusion.

How to Coordinate a Multi-Agent AI Setup

Multi-agent systems don’t just work because there are more models involved. They work because the agents understand their place in a structure. Communication protocols play a crucial role in enabling effective agent collaboration by providing frameworks for message passing, synchronization, and secure data exchange. Without clear coordination, adding more agents just multiplies confusion.

Here’s how we kept Omega aligned and scalable by enforcing coordination patterns from the start:

Role Assignment

Each agent should have a single, well-defined responsibility. This is the foundation. For Omega, we created a generation agent to write initial content, a critic agent to evaluate and improve it, and routing logic to decide when each was triggered. We avoided overloading agents with too many tasks—even if technically possible—because that quickly blurred accountability and made debugging harder.

Routing Logic

Omega listens in Slack. But not every message should activate every agent. That's where routing logic comes in. We built a lightweight intent classifier to analyze trigger phrases and route them to the right agent: generation, review, proposal suggestion, or document fetch. In early versions, we hardcoded this routing; later, we moved to a pattern-matching module that could be iterated independently of agent logic.

Interaction Protocols

We tested several interaction protocols—from basic chains to more complex feedback loops. Our most used was the primary-critic pattern: one agent completes a task, the other reviews and edits, with a fallback handler managing retries or escalating to a human. These protocols created boundaries and made the system more transparent—both for our team and for users wondering “what just happened?”

Real-World Multi-Agent AI Use Cases We've Proven

The value of multi-agent systems becomes most visible when tasks require multiple steps, distinct types of reasoning, or built-in feedback loops. Real-world examples include collaborative AI agents managing logistics, coordinating autonomous vehicles, or optimizing workflows in large organizations. Interesting areas for agentic AI applications range from automatic news updates and research summarization to complex data gathering workflows. Multi-agent systems can also automate gathering research from multiple sources, streamlining the process of compiling and summarizing information for tasks like news aggregation and academic reviews.

Sales: Proposal Generation

In Omega, proposal generation was one of the first areas where a multi-agent approach made a clear difference. A primary agent took care of drafting a feature list based on client briefs and historical project data. But even with prompt tuning, the quality varied—some responses were too generic, others missed the mark entirely. Once we introduced a critic agent to evaluate and refine the primary's output, quality improved significantly.

The critic helped flag unclear phrasing, missing elements, or off-tone responses. By splitting creation and evaluation, we reduced manual editing and improved consistency across proposals.

Support Workflows

Customer support scenarios also benefit from dividing responsibilities. While a single agent can handle simple inquiries, more complex cases require task separation. For example, one agent might first triage an incoming message to detect urgency and intent. Another might search available resources or draft a solution. If the issue doesn't match known cases, a third agent can handle escalation or request human intervention.

This structure ensures that each part of the workflow remains efficient and controllable, without overloading a single model with too much responsibility or context.

Data Analysis and Reporting

Data workflows often involve multiple distinct steps: fetching raw inputs, organizing the data, generating visuals, and summarizing insights. Integrating diverse data sources is crucial for robust analysis, as it allows the system to draw from a wide range of information and improves overall reliability. These steps are rarely best handled by a single agent—especially when clarity and reliability matter. Assigning each task to a separate agent allowed us to control output quality more tightly, and leveraging knowledge bases for information retrieval further enhanced the accuracy and relevance of the results. Additionally, multimodal models can be used to handle different data formats such as text, images, and audio within reporting workflows.

In internal experiments, this structure made it easier to update logic for a single part of the workflow without affecting everything else, which helped teams iterate faster and spot where results were drifting.

Sales AI agent production process: tasks

What to Watch Out For in Multi-Agent Systems

Multi-agent systems can offer real benefits—but they also introduce new risks. In our work on Omega, we quickly learned that more agents don’t always mean better outcomes.

Latency and cost were the first red flags. Every additional agent meant another LLM call, and if agents waited on each other, response times grew quickly. In some cases, three-agent chains tripled both cost and delay compared to a solo setup. We mitigated this by running agents in parallel where possible and setting strict timeouts.

We also encountered looping. In one case, a planner kept passing similar prompts back to the generation agent, resulting in an infinite refinement cycle. To catch this, we added step logging in Slack and hard-coded loop breakers after a certain number of turns. Eventually, we also introduced a fallback agent that could short-circuit uncertain flows with a predefined response.

Debugging multi-agent systems proved harder than we expected. When outputs failed or felt off, it wasn’t always clear which agent was at fault. To solve this, we relied heavily on Langfuse for observability, tracing which agent acted when, and with what input/output. We also kept prompts versioned in Git, so we could compare behavior across iterations and roll back when needed. As our systems grew in complexity, we adopted new tools like research pads, scratchpads, and programmatic logic to help manage and organize the increasing volume of data and agent interactions.

As multi-agent systems scale, the interactions between agents can lead to emergent behaviors—unexpected or complex outcomes that arise from decentralized collaboration and are not explicitly programmed. This makes careful monitoring and debugging even more critical.

And perhaps most importantly: we learned to resist overengineering. Some tasks tempted us to split logic too early—adding agents where a smarter prompt or better tooling would’ve done the job. We now treat multi-agent setups as a response to complexity, not a default.

Building Multi-Agent Systems in Practice

There’s no one-size-fits-all stack for building multi-agent systems, but a few tools and habits helped us build Omega efficiently and with confidence. We used AutoGen to structure our agent orchestration, which gave us primitives for role definition, turn-based interaction, and memory sharing.

Automating tasks with agentic AI, especially when leveraging large language models (LLMs), allowed us to use natural language instructions to route data and perform actions within workflows. Code generation played a key role in automating complex development tasks, simplifying the process and improving system efficiency. Reinforcement learning was also important for training and evaluating agents, though it has limitations in planning and creativity that highlight the need for new benchmarks in multi-agent systems.

On the monitoring side, L angfuse became essential. It allowed us to track inputs, outputs, and agent behavior in production. We set up alerts on loop counts, latency spikes, and rare failure modes—giving us early signals when something broke.

For local iteration, Promptfoo helped us A/B test prompt versions and compare agent chains side-by-side. This saved time and reduced uncertainty before going live.

To ensure reliability, we ran synthetic test cases before releasing new logic—feeding in edge-case briefs, ambiguous inputs, and broken metadata to test how agents behaved under pressure.

We also followed one rule: start small. Add agents only when a task clearly needs distinct logic, reasoning, or validation. Each new agent should exist for a reason—not just because the architecture allows it.

AI agent Tooling Summary

Purpose	Tool	What It Helped With
Agent Orchestration	AutoGen	Role setup, turn-taking, memory passing
Observability & Logging	Langfuse	Tracing inputs/outputs, latency tracking, loop detection
Prompt Iteration & Testing	Promptfoo	A/B testing prompts, comparing agent behaviors across setups
CI/CD	CircleCI	Deploying agent updates with safety checks and rollback options
Secure Secrets	AWS Systems Manager Parameter Store	Safeguarding API keys, user data, and internal tokens across services
Collaboration & Documentation	Google Docs	Sharing, storing, and summarizing research findings within multi-agent workflows, enabling seamless information exchange and collaboration

In summary, while chat interfaces represent an early stage of AI development focused on simple interactions, more advanced agent systems enable greater autonomy and complex problem-solving within multi-agent environments.

Human-in-the-Loop: Structuring Collaboration with Multi-Agent AI

Omega's development has shown that even with increasingly capable agents, human involvement remains essential.

Modern AI agents, including those used in Omega, can complete tasks, retrieve information, and generate structured outputs. But they still lack context, critical thinking, and the ability to assess risk or business relevance. That's why we've built Omega around a human-in-the-loop process, where agents act, but humans oversee and guide.

In practice, this looks like:

• Task definition by humans: Teams provide the initial instructions, goals, or prompts agents respond to.
• Output review: Before any result is applied in a real workflow—like sales outreach or proposal building—a human checks the content for relevance, accuracy, and tone.
• Feedback loops: If an agent response is incorrect, incomplete, or confusing, the team flags it. This feedback helps us improve the prompts, logic, or routing conditions in future iterations.
• Defined roles and guardrails: Agents in Omega are assigned specific roles—such as planner, executor, or critic—to break down complex tasks. We also enforce limits like max turns or response thresholds to prevent circular or runaway agent behavior.

“When agents face edge cases or conflicting data, human oversight is the fallback. It's how we prevent missteps before they escalate.” — Kuba Filipowski, CEO and Co-Founder at Netguru

While these workflows still require ongoing tuning, they allow us to use AI in meaningful ways—supporting teams without assuming too much autonomy. We are embedding verification steps and defining clear human roles, we make them practically useful.

Choose the Smallest Swarm That Works

The appeal of multi-agent systems is real. But the best ones don't start with complexity—they start with a task that justifies it.

In Omega, we didn't set out to build a swarm. We started with one agent, then added more only when it helped reduce friction or improve outcomes. That pattern worked for us—and we suspect it'll hold true for most teams.

As the system matured, so did the AI agent tech stack behind it

So if you're building your own system, don't begin with a multi-agent plan. Begin with a workflow that matters. Split it when needed. Coordinate only what you must. And when the added structure leads to faster iteration, better quality, or fewer errors—you'll know the swarm was worth it.