{"id":34135,"date":"2026-11-10T14:30:13","date_gmt":"2026-11-10T13:30:13","guid":{"rendered":"https:\/\/msg-life.sk\/?p=34135"},"modified":"2026-11-11T06:09:25","modified_gmt":"2026-11-11T05:09:25","slug":"multi-agent-systems-architectures-frameworks-and-where-humans-decide-in-insurance","status":"publish","type":"post","link":"https:\/\/msg-life.sk\/en\/articles\/ai\/multi-agent-systems-insurance\/","title":{"rendered":"Multi-agent systems: Architectures, frameworks, and where humans decide in insurance"},"content":{"rendered":"

Multi-agent systems split a complex task across several coordinating agents instead of one program. That flexibility has a price: less predictability and auditability, which are the two properties a regulated insurer weighs most. This article covers the five design choices that decide whether the pattern fits. The architecture, centralized or decentralized, shapes your audit trail and how the system fails. The agent type, reactive, deliberative, or hybrid, trades latency for adaptability. The framework sets how much orchestration you write yourself. The learning approach decides whether the system improves from data or stays fixed between releases. In insurance, the fifth choice is where human decisions sit. For claims and underwriting, the article adds an orchestrator-specialists-human design pattern. It shows where that pattern fails and which controls keep it supervisable.<\/p>\n

Conceptual framing: Multi-agent vs.\u00a0single-agent systems<\/h2>\n

A single-agent system concentrates reasoning, tool use, and decisions in one model instance. A multi-agent system distributes that work across several autonomous, interacting agents in a shared environment. The difference matters architecturally. A single agent either completes the task or fails it, while a multi-agent design can degrade gracefully if individual agents fail, provided coordination is designed for it.<\/p>\n

Multi-agent systems (MAS) are valuable for large-scale, complex tasks that can involve hundreds or thousands of agents<\/a>, but that scale brings coordination overhead absent from single-agent designs. Each agent operates with a degree of autonomy<\/a> and can act and decide independently.<\/p>\n

How agents are classified<\/h3>\n

Agents are commonly classified by how they decide. Reactive<\/strong> agents respond to stimuli through condition-action rules without internal state. Deliberative<\/strong> agents keep an internal model of the world and plan before acting. Hybrid<\/strong> agents combine both: reactive behavior for time-critical responses, deliberation for longer-horizon planning. Each agent in a MAS has individual properties, while all agents behave collaboratively to produce the desired global properties.<\/p>\n\n\n\n\n\n\n\n\n
Single-agent vs. multi-agent systems: control, failure mode, and coordination overhead<\/caption>\n
Dimension<\/th>\nSingle-agent system<\/th>\nMulti-agent system<\/th>\n<\/tr>\n<\/thead>\n
Control<\/td>\nCentralized in one model or process<\/td>\nDistributed across several agents<\/td>\n<\/tr>\n
Failure mode<\/td>\nTotal task failure<\/td>\nPartial degradation possible<\/td>\n<\/tr>\n
Coordination overhead<\/td>\nNone<\/td>\nRequired (communication, negotiation)<\/td>\n<\/tr>\n
Best for<\/td>\nNarrow, well-defined tasks<\/td>\nComplex tasks that decompose into parallel subtasks<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Google Cloud notes that single-agent systems suit tasks where interaction is minimal and centralized control is efficient, such as recommendation engines or fraud detection, and that they are simpler to develop, with lower maintenance costs and more predictable outcomes. That is a useful default: start with one agent, and add agents only when the task genuinely decomposes.<\/p>\n

Architectures and components of multi-agent systems<\/h2>\n

MAS can operate under centralized or decentralized architectures. In a centralized network, a central unit contains the global knowledge base, connects the agents, and oversees their information. This simplifies auditability, because every decision passes through a traceable coordination point.<\/p>\n

In a decentralized network, agents coordinate peer-to-peer without a single point of control. That improves fault tolerance but makes the decision path harder to reconstruct afterward. A hierarchical arrangement, with agents at different levels of autonomy, works as a hybrid of the two.<\/p>\n

\"Multi-agent
Centralized and decentralized multi-agent architectures. In the centralized layout, agents A, B, and C connect only to a coordinator that holds shared state and routes every message. In the decentralized layout, the same agents connect directly to each other, each with partial local state.<\/figcaption><\/figure>\n

Every MAS, whatever its architecture, has four core components:<\/p>\n