When to Use Multi-Agent Systems: Choosing Between Solo and Multi-Agent 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..
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. Omega evolved into a multi-agent system—and that shift unlocked a new level of reliability, clarity, and performance.
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. Each agent in the system performs a specific role, such as planning, writing, validating, or retrieving data.
In contrast to a single-agent approach, a multi-agent LLM system distributes the load, allowing more specialized reasoning and structured workflows.
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. 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.
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. This setup typically relies on a single language model or decision-making loop and works well for focused, 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
Multi-Agent System
A multi-agent system involves multiple AI agents working together toward the same goal. 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.
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.
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.
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.
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.
Why? Because we were pushing one agent to do everything: reason, fetch, generate, format, critique—and do it all in a single loop.
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 |
| Cost & Latency | Lower inference cost and response time | Potentially higher due to multiple passes |
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.
- Logic errors or hallucinations increase with context size.
- 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 they become valuable when a task exceeds what a single agent can handle reliably. 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.
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.
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.
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. 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.
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. 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.
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.
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.
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.
On the monitoring side, Langfuse 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 |
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.
