Single Agent vs Multi-Agent: The Architectural Decision That’ll Make or Break Your AI Project

We have a new project where we’re going to build… yes, you got it—an agent. Or maybe many agents? That’s exactly the question we needed to answer, and spoiler alert: the answer isn’t as obvious as your last Tinder swipe.

We conducted thorough research and design sessions to determine the best approach. Here’s what we found, complete with the battle scars and “oh shit” moments that came with it. I hope you can benefit from our findings without stepping on the same architectural landmines.

When building AI-powered systems, one of the fundamental architectural decisions is whether to implement a single comprehensive agent or multiple specialized agents working together. This choice significantly impacts performance, maintainability, user experience, and—let’s be honest—your sanity as a developer.

But first, let’s establish what we mean by an AI agent, because apparently everyone and their startup has a different definition.

What is an Agent?

In the context of AI, specifically generative AI, an agent is software that can perform actions semi-autonomously or fully autonomously based on a set of instructions and can interact using natural language. Think of it as ChatGPT that actually does things instead of just talking about doing things.

Modern AI agents are built on Large Language Models (LLMs) enhanced with function calling capabilities. This functionality allows chatbots to perform actions beyond text generation—they can execute code, interact with APIs, modify files, and integrate with external systems. What started as simple Python code execution has evolved into sophisticated systems like Model Context Protocol (MCP) servers that enable agents to interact with virtually any external service. It’s like giving your chatbot hands, and sometimes those hands have hammers.

So, what exactly defines an agent? The definition is somewhat fluid (shocking, I know—tech definitions being vague), but in my view, an AI agent is software that can:

• Execute actions semi-autonomously or fully autonomously
• Follow a set of instructions or goals
• Interact using human natural language
• Make decisions based on context and feedback

This broad definition could include everything from a grammar correction tool to a comprehensive marketing assistant—the possibilities are extensive, and the marketing departments (and CEOs with quarterly “optimization” coff coff, layoffs targets) are salivating.

The key distinction in development environments like VS Code is between “Ask” mode (traditional chat) and “Agent” mode. In Ask mode, the AI can only provide suggestions and answers—it’s basically a very expensive rubber duck. In Agent mode, it can automatically modify files, execute commands, and potentially burn down your codebase in a blink if not properly configured. This autonomous capability is what makes agents both powerful and potentially the reason you’ll be working weekends.

What is a Multi-Agent Design?

A single agent is essentially a monolithic piece of software—one component handling all functionality. It’s the AI equivalent of that one coworker who insists they can handle “everything” and usually can’t.

A multi-agent system consists of multiple specialized agents communicating with each other to accomplish complex tasks. The comparison to microservices is apt here, though agents don’t need to be “micro”—hence the term “multi-agent” rather than “micro-agent.” Because apparently we learned nothing from the microservices hype cycle.

I researched what others in the field are saying about this. For instance, my colleague has written extensively about agent sizing, arguing that micro-agents don’t make sense due to orchestration complexity, while overly large agents become difficult to manage. There should be clear principles for determining when an agent becomes too large—basically, the “you know it when you see it” approach to software architecture.

One critical limitation is function calling capacity. Agents typically become overwhelmed when dealing with more than 10-20 function calls. They start selecting inappropriate tools and their performance degrades significantly. This happens because function calling relies on tool descriptions, and it’s much easier to choose between 2-3 tools than among 100 options. It’s decision paralysis, but for robots.

The technical implementation compounds this problem. Since LLMs are stateless, you must pass complete function descriptions, parameters, and arguments as extensive JSON in the context window for every request. There’s no magic here—the model literally reads through all available function definitions and tries to match them to your request. While companies often absorb or hide these token costs, the underlying issue remains: LLMs get cognitively overwhelmed when parsing through dozens of function options, leading to poor selection decisions. It’s like having to reintroduce yourself and explain every tool in your toolbox at every meeting because your AI colleague has the memory retention of a particularly forgetful goldfish.

So Which Approach is Better?

I’ve built agents before, but nothing as complex as what we’re planning for production. The tooling considerations alone are substantial.

According to conversations with Microsoft engineers (though this isn’t officially validated, so take it with a grain of corporate salt), Microsoft deliberately chose not to create a single orchestrating agent in Copilot. Instead, in Copilot Studio, you create multiple independent specialized agents, and users select which one they want to interact with. This approach is primarily driven by UX considerations—humans are naturally wired to interact with specific experts rather than some omnipresent AI overlord who claims to know everything. It eliminates the need for complex orchestration while providing a more intuitive user experience: just create domain-specific agents and let users pick their poison.

And for a reason, the multi-agent approach has its own complexities. Drawing from my experience with monolith vs microservices (where 90% of the time the answer is “it depends,” and the other 10% is “you should have asked this question six months ago”), there’s a crucial difference here: agents are non-deterministic.

In microservices, you can reliably test individual units and perform integration testing to ensure predictable behavior. With agents, this becomes significantly more challenging—it’s like trying to unit test a creative writing class. Microsoft engineers shared an interesting case where a single agent worked perfectly in isolation, but failed spectacularly when integrated into a multi-agent system. The orchestrating agent was inadvertently filtering out critical pieces of user request data that the specialized agent needed to function properly. It’s the AI equivalent of playing telephone with important business requirements.

Trade-offs to Consider

Here’s the brutal reality check you’ve been waiting for:

Multi-agent challenges:

• Additional observability, logging, security, and tracing complexity (because debugging one black box wasn’t hard enough)
• Each agent layer adds an extra LLM call, increasing latency and cost (your AWS bill will thank you)
• Non-deterministic behavior makes testing and debugging difficult (good luck explaining this to QA)
• Orchestration complexity can introduce unexpected failure modes (surprise! Your AI just became sentient and decided to take a coffee break)

Single agent challenges:

• Function calling limitations (performance degrades beyond 10-20 functions like a smartphone after two years)
• Increased complexity in a monolithic structure (the “god object” problem, but with AI)
• Harder to maintain and update specific capabilities (changing one thing breaks three others)
• Context window limitations with extensive tool descriptions

Recap and Recommendations

After thorough research, analysis, and a few existential crises about the nature of artificial intelligence, here’s what I recommend:

Choose Single Agent When:

• Your use case requires fewer than 15-20 function calls
• You need predictable, deterministic behavior (good luck with that)
• You’re building a focused, domain-specific solution
• You want to minimize latency and cost
• Your team has limited experience with agent orchestration
• You value your sleep and mental health

Choose Multi-Agent When:

• You need to handle diverse, complex workflows that exceed function calling limits
• You have distinct domains that require specialized expertise
• You can afford the additional complexity and cost (both financial and psychological)
• You have robust observability and testing infrastructure
• User experience benefits from specialized interactions
• You enjoy debugging distributed systems with non-deterministic behavior

Key Takeaways

Start Simple: Begin with a single agent and evolve to multi-agent only when you hit clear limitations. It’s easier to split than to merge, unlike your last relationship. 1.

Function Limits Matter: The 10-20 function calling threshold is real and impacts performance significantly. Respect the limit, or the limit will disrespect you. 1.

Non-deterministic Nature: Unlike microservices, agents require different testing and debugging approaches. Traditional testing strategies go out the window faster than your productivity after discovering TikTok. 1.

Cost Considerations: Each agent layer adds LLM calls—budget accordingly. Your CFO will either love you or hate you, probably both. 1.

Orchestration is Hard: Multi-agent coordination introduces complex failure modes that would make a Byzantine general weep.

The decision ultimately depends on your specific requirements, team capabilities, and tolerance for complexity. Like the microservices debate, there’s no universal answer—but understanding these trade-offs will help you make an informed choice instead of a panicked one at 2 AM when everything’s on fire.

Remember: you can always start with a single agent and refactor to multi-agent as your requirements evolve. This is what we are going to do. The key is building with future flexibility in mind while avoiding premature optimization. Because the only thing worse than over-engineering is under-engineering and then frantically re-engineering when your simple solution becomes the bottleneck for your entire product.

Choose wisely, debug extensively, and may the odds of deterministic behavior be ever in your favor.