An AI orchestrator is a coordinating system or agent that manages the execution of a multi-step or multi-agent process, deciding task sequencing, delegating sub-tasks to specialized agents or tools, and integrating their results into a final outcome.
As agentic systems grow beyond a single model making a single decision, something has to manage the bigger picture: which sub-task runs next, which specialized agent or tool is best suited for it, how results from parallel sub-tasks get merged, and when the overall goal is actually satisfied. The orchestrator holds that state so individual sub-agents can stay narrowly focused on their own piece without needing awareness of the whole plan.
A well-designed orchestrator decomposes work into genuinely independent pieces where possible, since overlapping work wastes resources and creates conflicting edits or duplicate actions, and it tracks progress durably enough that a partially completed multi-step process can resume correctly even if something is interrupted midway. It also has to decide, sometimes dynamically, whether a task is best handled by one deep, cohesive agent or by fanning it out across several specialized ones.
Neotask's own multi-agent orchestration model treats the top-level coordinating session as the orchestrator, it plans, splits work into non-overlapping parts across specialized workers, and integrates each result deliberately, exactly the pattern an AI orchestrator applies at the level of a single tenant's complex task.
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