An agent swarm is a group of multiple AI agents working concurrently on related sub-tasks, coordinating through shared state, messages, or a supervising orchestrator, rather than one agent handling everything sequentially.
Swarms exist because many real problems decompose into independent pieces that benefit from parallel execution: a coding task might split into research, implementation, and test-writing agents; a data pipeline might fan out one agent per data source. Running these concurrently cuts wall-clock time dramatically compared to a single agent working through the same list serially.
The hard part of a swarm isn't spinning up parallel workers, it's coordination: making sure agents don't clobber each other's edits, that results get merged deliberately, and that a supervising layer tracks which sub-tasks are done, failed, or still running. Swarms typically need a shared ledger or task list, non-overlapping work boundaries, and a reconciliation step where a human or lead agent reviews and integrates the pieces.
Neotask's orchestration layer can fan a large task out across several sub-agents at once, each assigned a distinct file or module so their work never overlaps, then integrates each result before moving to the next phase. This mirrors how the platform's own multi-account orchestration model splits big undertakings into independent parts run concurrently.
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