When to split AI work into subagents, and what they need to share

Some divisions reduce waiting; others add handoffs

Researching three products independently can proceed concurrently. Investigating a defect, fixing it, and testing the fix requires later steps to wait for earlier results. Both jobs can be split into three parts, but splitting alone does not shorten completion time.

A subagent receives a bounded task from a lead agent. Using announcements and design material available by March 30, 2026, this article considers when that division helps and when it adds handoff costs. The product-comparison task below is hypothetical.

Separate smaller-model delegation from parallel execution

OpenAI's March 17 GPT-5.4 mini and nano announcement describes a larger model handling planning and synthesis while mini performs narrower work such as code exploration. This is a concrete use of task-specific model selection. Choosing a smaller model and running tasks concurrently are separate decisions; delegation can also be sequential.

Anthropic's June 2025 research-system article reports benefits from exploring independent directions while noting difficulties with heavily interdependent work. In its data, multi-agent systems used about 15 times the tokens of ordinary chats. That comparison is with chats, not single-agent systems. It is not a universal cost multiplier, but it challenges the claim that delegation necessarily costs less.

Even if the three product investigations run concurrently, preparing assignments, synthesizing findings, and filling gaps remain. Evaluate the lead and subagents' total usage, tool delays, and retries alongside the smaller model's unit price.

Share comparison criteria and return separately traceable findings

“Research product A” leaves too much unspecified. I recommend defining the use case, version, comparison fields, source URLs, and treatment of unknowns. Combining one agent's free-tier findings with another's enterprise-tier findings does not produce a sound comparison.

Ask each agent to return facts, sources, and unresolved questions, then make the final judgment once the products can be compared under matching conditions. Retaining access to original evidence allows conflicting summaries to be checked. Agreement among agents is not independent corroboration if they all relied on the same incorrect source.

Role labels also do not enforce permissions. A research-only task should be configured without unnecessary write operations. Where agents need not edit one shared file, returning separate results for the lead to integrate can avoid write conflicts.

Keep delegation only if comparison with one agent supports it

Known execution sequences can use fixed workflows. Google ADK's workflow documentation describes sequential, parallel, and loop control without asking a model to make each orchestration decision. Before adding a coordinator, distinguish exploratory delegation from executing an established procedure.

For product research, give single-agent and delegated configurations the same inputs and completion criteria. Compare factual accuracy, missing fields, total time, cost, and rework. Revise the split if integration dominates; retain it if independent investigations produce a demonstrated benefit. My recommendation is to choose the structure from task dependencies and measured results.