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Agents24 September 2026·6 min read

One agent or several? When splitting the work actually helps

Multi-agent designs look elegant and often add cost and failure points. A test for when specialization earns its keep.

Splitting a task across several agents sounds like good engineering, with each one specialized. In practice it adds hand-offs, latency, cost and places for context to get lost. Start with one agent and split only for a reason.

Reasons that justify a split

  • Different permissions — one component must read sensitive data, another must act externally, and they should not share access.
  • Different tools or context that would overload a single prompt.
  • Independent work that can run in parallel and be merged.
  • A separate checker — an independent step that reviews another's output.

Reasons that don't

  • The diagram looks cleaner.
  • Each role has a different name in the business.
  • A framework makes it easy.

Compare the single-agent and multi-agent versions on the same test set, counting accuracy, latency and cost. Keep the extra moving parts only if the numbers justify them.

Related capability

This is what our Autonomous AI Agents practice is built around.

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