Workflow orchestration is the coordination layer that manages how multiple independent tasks, services, or agents run together in the correct order, with the right data handed between them, to complete a larger process.
Orchestration is distinct from a single automated task in that it manages dependencies across many moving parts: task B can't start until task A finishes, task C needs to run in parallel with task D, and if task E fails the orchestrator needs to retry it or route to a fallback rather than letting the whole pipeline collapse. Orchestration engines typically model this as a directed graph of steps, tracking the state of each node and only advancing when its prerequisites are satisfied.
In multi-agent AI systems specifically, orchestration takes on an extra dimension: deciding which specialized agent or tool should handle a given sub-task, passing context between agents without losing information, and merging their outputs into a single coherent result. This is harder than orchestrating deterministic microservices because agent outputs are probabilistic, so the orchestrator also needs validation logic to catch and correct low-quality intermediate results before they propagate downstream.
When a Neotask company task spans research, drafting, and review, the orchestration layer sequences which specialized agent handles each phase, passes the accumulated context forward, and holds the task in a retryable state if any phase fails rather than losing the work already done. Human approval gates can be inserted at any point in that graph.
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