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A message broker is the software component that receives messages from producers and delivers them to consumers, acting as a reliable intermediary between systems that never talk directly. It is the post office of distributed architectures: it accepts, stores, routes and delivers, guaranteeing messages arrive even when the recipient is temporarily away.
The broker implements the messaging patterns: point-to-point queues (each message to exactly one consumer) and pub/sub (each message to every topic subscriber). It offers configurable delivery guarantees (at most once, at least once, exactly once), disk persistence, acknowledgements and dead letter queues for messages that repeatedly fail. RabbitMQ, Apache Kafka, ActiveMQ and managed cloud services are the best-known examples.
In AI, brokers orchestrate asynchronous workloads: generation requests queue up toward GPU workers, domain events trigger embedding and indexing pipelines, multi-agent systems exchange tasks and results through the broker. Beyond AI they have integrated ERPs, e-commerce and banking systems for decades.
The word broker comes from commerce: the intermediary between buyer and seller, a word of medieval Anglo-French origin. Enterprise messaging has a milestone in IBM MQSeries (1993), which brought reliable queues to corporate systems; the next generation is open source, with Apache ActiveMQ (2004), RabbitMQ (2007) and Kafka (born at LinkedIn, open source since 2011), which redefined the category as a distributed event log.
Grace's professional scenarios on system integration ask you to describe to an AI how to make different applications talk to each other: the message broker is the component to name. In problem solving you train exactly the ability to design intermediaries that make processes reliable.
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