A distributed system consists of multiple processes or machines that communicate over a network and work toward a shared result. Each component has only part of the state, messages can be delayed or duplicated, and one component can fail while others continue running.
Background AI workflows inherit these ordinary systems problems when schedules, models, queues, tools and storage operate independently. Durable event records, idempotency, retries and explicit boundaries help prevent duplicate work, lost inputs and uncertain execution history.
Acronyms and aliases
distributed systems variant
Specialised terms
Related terms
Frequently asked questions
Why is an AI agent runtime a distributed system?
It often coordinates schedulers, models, tools, queues, databases and external services that communicate and fail independently.
What failures are common in distributed systems?
Common failures include delayed or duplicate messages, partial outages, inconsistent state, lost work and retries that repeat an action.
