Multi-Agent Systems: Architecture Before the Number of Agents

Agentic AI is designed to deliver measurable added value without hastily deploying many different AI agents only to realize later that costs and controllability suffer. Initiatives rarely fail because of the use case, but rather because of architectural considerations: Which architecture is best suited for the task, and how can this be justified using key metrics?

Recent research offers a helpful perspective on this: It systematically compares multi-agent architectures and shows when multi-agent systems actually scale and when additional AI agents can actually degrade performance—for example, due to coordination overhead or error chains. The key takeaway: Scalability is primarily a matter of architecture and governance, not a question of the number of agents.

A multi-agent system (MAS) connects several specialized AI agents that perform tasks autonomously and coordinate with one another. Instead of a single agent handling an entire process, the system distributes various subtasks among multiple agents, who work together toward an overarching goal.

From PoC to Multi-Agent System: Why Coordination Must Become Measurable

Many projects start as a PoC: an AI agent, a tool, a demo workflow. As the project scales, multiple AI agents are quickly added—often with roles such as research, planning, or validation. This seems obvious, but it becomes risky if one key point is underestimated: An AI agent is not just a chat interface. It is an actor that calls up tools, data, and prepares or triggers decisions. This increases the demands on control, quality assurance, cost management, and traceability—not just in theory, but in practice.

The choice of agent architecture can be structured using coordination metrics, for example:

• Overhead (Coordination, Handoffs, Conflicts)
• Efficiency (Output per Budget)
• Redundancy (Duplication of Work)
• Error amplification (propagation of incorrect intermediate results)

Here, we compare five architectural types (Single, Independent, Centralized, Decentralized, Hybrid) across a wide range of configurations in order to isolate architectural effects as clearly as possible from other factors.

Instead of making multi-agent systems the standard, a repeatable process is helpful: determine the task class, derive the architecture, define quality gates, and then manage the process using a few clear metrics. The key is not “more parallel,” but “targeted parallelism”—where it really makes a difference. And with a body that actively monitors quality and risk. Coordination isn’t free: there are saturation effects once a certain performance level is reached. Furthermore, the topology determines whether errors in the chain are amplified or caught early on.

Coordination Overhead
More AI agents mean handoffs, coordination, repetitions, and conflict resolution. With a fixed budget (time/tokens/compute), a multi-agent system can suffer disproportionately—especially in tool- and integration-heavy workflows—because coordination ties up resources that are then lacking for the actual problem-solving. Rule of thumb: When tools are the bottleneck, “more agents” often help less than better orchestration plus clear limits.

Capability Saturation
Starting from a solid single-agent baseline, additional AI agents often yield only minimal—or even negative—benefits. In practice: If a single AI agent is already performing a task reliably, multi-agent systems quickly become a source of complexity and operational costs without delivering a reliable improvement in quality. Therefore, they are not a default upgrade, but rather an investment that must be carefully weighed.

Error Amplification
Error propagation is particularly critical: In uncoordinated designs—such as peer-to-peer chains without intermediate checks—small errors at the beginning of a chain quickly cause serious errors in subsequent steps, which then end up “unchecked” in the final result. Centralized coordination can also limit errors significantly better than independent AI agents whose results are only consolidated at the end. Operational conclusion: Without a quality gate, not only does the output scale, but so does the risk.

Architecture Comparison

In addition to the matrix, a brief overview helps illustrate the “strength vs. risk” for each topology. It is important not to automatically equate Independent with “quickly scalable”—especially if actions are to be triggered at the end. In practice, these architecture types also appear under other names—such as the supervisor-worker pattern (corresponding to “Centralized”) or the generator-critic loop (a hybrid pattern).

Coordination, overhead, and error amplification are therefore not side effects, but rather key scaling factors that must be actively managed.

Single-agent system in customer service:

The evaluation and preprocessing of incoming tickets should be carried out quickly and cost-effectively. This requires only a few tools, and no business-critical actions are performed. A single-agent solution would fully cover this scenario, whereas multiple agents would result in unnecessary token consumption and longer processing times.

Multi-Agent System for Claims Management in Insurance Companies

A complex, business-critical process that involves receiving, verifying, coordinating, and processing sensitive data, as well as interacting with numerous tools, should be centrally coordinated and controlled, while specialized agents handle the tasks as they arise.

A robust vision for operations

A viable standard workflow can be described regardless of industry. It begins with a clear mandate: scope, data classes, and target criteria. This is followed by orchestration: Which subtasks make sense? What are the applicable budgets? Which tools are permitted? When should an issue be escalated?
During execution, evidence is collected and recommendations are generated. The next step is crucial: A quality gate checks for consistency and compliance with rules and, if necessary, requires human approval. Only then may actions take effect. The entire process is made measurable through observability, so that costs, error rates, and rework are not only visible after the fact.

Sources:
[2512.08296] Towards a Science of Scaling Agent Systems