Short answer
A multi-agent system is an architecture in which several specialized AI agents work together, each handling a distinct task and coordinating to complete a larger goal. Instead of one model doing everything, work is divided among agents that pass context and decisions between them.In customer experience, this might mean separate agents for classifying an inquiry, retrieving account context, drafting a resolution, and checking it before it goes out. The appeal is specialization. The risk is that more moving parts can mean more places for an unverified action to slip through.
Aide, the AI platform for customer service, treats the number of agents as an implementation detail, not the headline. What matters is the intent. Classification of every message against the team's own topics, learning from the examples the team adds, decides which intents are safe to automate. Each automated path is scoped to a single intent and tested on real conversations before anything runs live, so no agent in the chain can act on an unverified intent and added complexity never becomes added risk. However the work is divided, the intent map remains the single picture of customer demand. A multi-agent system should make resolution more reliable, not harder to trust.