‘More agents’ not a reliable path to better enterprise AI systems, study finds

Researchers from Google and MIT conducted a complete analysis agentic systems and the dynamics between the number of agents, coordination structure, model capacity and task properties. Although the prevailing sentiment in the industry is "more agents are all you need," Research suggests that scaling agent teams is not a guaranteed path to better performance.

Based on their results, the researchers defined a quantitative model capable of predicting the performance of a agent system on an invisible task. Their work reveals that adding additional agents and tools acts as a double-edged sword: while it can unlock performance on specific problems, it often introduces unnecessary overhead and diminishing returns on others.

These findings provide a critical roadmap for developers and business decision-makers trying to determine when to deploy complex multi-agent architectures versus simpler, more cost-effective single-agent solutions.

The state of agent systems

To understand the implications of the study, it is necessary to distinguish between the two main architectures used today. Single-agent systems (SAS) present a solitary place of reasoning. In this configuration, all perceptions, planning, and actions occur in a single sequential loop controlled by a single LLM instance, even when the system uses tools, self-reflection, or chain-of-thought (CoT) reasoning. Conversely, a multi-agent system (MAS) comprises multiple LLM-supported agents communicating via structured message passing, shared memory, or orchestrated protocols.

The business sector has seen a renewed interest in MASmotivated by the principle that specialized collaboration can systematically outperform single-agent systems. As tasks become increasingly complex and require sustained interaction with environments (e.g., coding wizards or financial analysis bots), developers often assume that distributing work between "specialist" agents are the superior approach.

However, researchers argue that despite this rapid adoption, there remains no principled quantitative framework for predicting when adding agents boosts performance and when it erodes it.

A key contribution of this article is the distinction between "static" And "agentic" tasks. The researchers applied a "Agent Reference Checklist" differentiate tasks that require sustained multi-step interactions, iterative information gathering, and adaptive strategy refinement from those that do not. This distinction is vital because strategies that work for static problem solving (like voting on a coding quiz) often fail when applied to real agentic tasks where "coordination costs” and “error propagation” can propagate through the problem-solving process.

Testing the limits of collaboration

To isolate the specific effects of system architecture, the researchers designed a rigorous experimental framework. They tested 180 unique configurations involving five distinct architectures, three LLM families (OpenAI, Google and Anthropic) and four agent benchmarks. The architectures included a single-agent control group and four multi-agent variants: independent (parallel agents without communication), centralized (agents reporting to an orchestrator), decentralized (peer debate), and hybrid (a mix of hierarchy and peer communication).

The study was designed to eliminate "implementation confuses" by standardizing tools, prompt structures, and token budgets. This ensured that if a multi-agent system outperformed a single agent, the gain could be attributed to the coordination structure rather than access to better tools or more computation.

The results call into question the "more is better" narrative. The evaluation reveals that the effectiveness of multi-agent systems is governed by "quantifiable trade-offs between architectural properties and task characteristics." The researchers identified three dominant models behind these results:

Tool-coordination compromise: With fixed computational budgets, multi-agent systems suffer from context fragmentation. When a computational budget is distributed among multiple agents, each agent ends up with insufficient capacity for tool orchestration compared to a single agent that maintains a unified memory flow.

Therefore, in tool-intensive environments with more than 10 tools, the effectiveness of multi-agent systems drops sharply. The researcher found that tool-intensive tasks suffer a 2-6x efficiency penalty when using multi-agent systems compared to single agents. Simpler architectures paradoxically become more efficient because they avoid coordination costs that worsen with environmental complexity.

Capacity saturation: The data established an empirical accuracy threshold of approximately 45% for single-agent performance. Once a single agent’s baseline exceeds this level, adding additional agents typically produces diminishing or negative returns.

However, co-author Xin Liu, a research scientist at Google and co-author of the paper, noted a crucial nuance for companies adopting it. "Businesses should invest in both [single- and multi-agent systems]” he told VentureBeat. “Better baseline models increase the baseline, but for tasks with natural potential for decomposability and parallelization (like our financial agent benchmark with an improvement of +80.9%), multi-agent coordination continues to provide substantial value, regardless of model capability."

Topology dependent error: The structure of the agent team determines whether errors are corrected or multiplied. In "independent" In systems in which agents work in parallel without communicating, errors were amplified 17.2 times compared to the single-agent baseline. In contrast, centralized architectures contained this amplification at 4.4 times.

"The key differentiator is having a dedicated validation bottleneck that catches errors before they propagate to the final result," said lead author Yubin Kim, a doctoral student at MIT. "In case of logical contradictions, “centralized” reduces the base rate… [by] 36.4%… For context omission errors, “centralized” reduces… [by] 66.8%."

Actionable insights for enterprise deployment

For developers and business leaders, these findings offer specific guidelines for creating more effective AI systems.

• THE "sequentiality" ruler: Before building a team of agents, analyze the dependency structure of your task. The strongest predictor of multi-agent failure is strictly sequential tasks. If step B relies entirely on the perfect execution of step A, a single-agent system is probably the best choice. In these scenarios, errors cascade rather than cancel each other out. Conversely, if the task is parallel or decomposable (for example, analyzing three different financial reports simultaneously), multi-agent systems offer considerable gains.

• Don’t Fix What’s Not Broken: Companies should always start by benchmarking themselves against a single agent. If a single-agent system achieves a success rate greater than 45% on a specific task that cannot be easily broken down, adding more agents will likely degrade performance and increase costs without providing value.

• Count your APIs: Use extreme caution when applying multi-agent systems to tasks that require many separate tools. The distribution of a symbolic budget between several agents fragments their memory and their context. "For tool-intensive integrations with more than about 10 tools, single-agent systems are probably preferable," Kim said, noting that the study observed a "2 to 6x efficiency penalty" for multi-agent variants in these scenarios.

• Match topology to objective: If a multi-agent system is required, the topology must match the specific objective. For tasks requiring high accuracy and precision, such as finance or coding, centralized coordination is superior because the orchestrator provides a necessary verification layer. For tasks requiring exploration, such as dynamic web browsing, decentralized coordination excels by allowing agents to explore different paths simultaneously.

• THE "Rule of 4": While it may be tempting to build massive swarms, the study found that effective team sizes are currently limited to around three or four agents. "The limit of three to four agents that we identify arises from measurable resource constraints," Kim said. Beyond this, communication overhead increases super-linearly (especially with an exponent of 1.724), meaning that the cost of coordination quickly exceeds the value of the added reasoning.

Future Perspectives: Exceeding the Bandwidth Limit

Even if current architectures reach a ceiling for small teams, this is likely a constraint of current protocols rather than a fundamental limitation of AI. The effective limit of multi-agent systems comes from the fact that agents currently communicate in a dense and resource-intensive manner.

“We believe this is a current constraint and not a permanent cap,” Kim said, highlighting some key innovations that can unlock the potential for agent collaboration at scale:

Sparse communication protocols: “Our data shows that message density saturates at around 0.39 messages per round, beyond which additional messages add redundancy rather than new information. Smarter routing could reduce overhead,” he said.

Hierarchical decomposition: Rather than flat swarms of 100 agents, nested coordination structures could divide the communication graph.

Asynchronous coordination: “Our experiments used synchronous protocols, and asynchronous designs could reduce blocking overhead,” he said.

Capacity-aware routing: “Our heterogeneity experiments suggest that strategically mixing model capabilities can improve efficiency,” Kim said.

This is something to look forward to in 2026. Until then, for the enterprise architect, the data is clear: smaller, smarter, more structured teams win.

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