Claude Code Subagents: When to Split Work vs. Run a Single Agent

Subagents get pitched as a free performance upgrade: split a task across multiple agents, get it done faster. In practice, every subagent you spin up costs context, coordination, and a real chance the pieces don’t recombine cleanly. The decision isn’t “should I use subagents,” it’s “does this specific task actually parallelize,” and most tasks don’t.

What a Subagent Actually Costs

A subagent isn’t a free thread. Each one needs its own context window, its own understanding of the relevant part of the codebase, and a way to report results back to whatever orchestrates it. That orchestration has to read, reconcile, and act on every subagent’s output, which is real work, not a formality. For a small task, that overhead outweighs any speed gained from parallelism.

When Splitting Actually Helps

• The subtasks are genuinely independent. Reviewing five unrelated files for a specific pattern, running the same fix across multiple isolated modules, researching several unrelated questions before synthesizing an answer. If subtask B needs to know what subtask A found, they aren’t independent, and running them in parallel just means you’ll redo work reconciling conflicting assumptions.
• The work is read-heavy, not write-heavy. Search, research, and analysis tasks parallelize well because there’s no shared state to conflict over. Code edits across the same file, or a shared module, do not.
• Each piece is well-scoped enough to hand off without extensive context. If briefing a subagent takes as long as just doing the task yourself, delegation isn’t saving anything.

When a Single Agent Is the Right Call

• The task requires accumulating understanding as it goes. Debugging a subtle bug, refactoring code where each change informs the next, anything where step 5 depends on what was learned in step 2. Splitting this loses the accumulated context that makes the later steps fast and correct.
• The subtasks touch the same files. Two subagents editing the same file in parallel is a recipe for one of them silently overwriting the other’s work, or a merge conflict that costs more time to resolve than serial execution would have taken.
• The task is small enough that orchestration overhead exceeds the work itself. A five-minute task doesn’t need a coordinator.

A Practical Test Before Splitting

Ask: if I described subtask A to someone with zero knowledge of subtask B, could they complete it correctly? If yes, it’s a real candidate for a subagent. If the honest answer is “well, they’d need to know what the other one is doing,” it isn’t parallel work, it’s one task that’s been cut in half, and cutting it in half doesn’t make it two tasks.

Common Mistakes

• Splitting a task by file count instead of by independence. Ten files doesn’t mean ten independent subtasks if all ten depend on the same shared type definition changing first.
• No plan for reconciling conflicting results. If two subagents could plausibly reach different conclusions about the same question, someone (or something) needs to own resolving that conflict, and that ownership needs to be decided before dispatching, not after.
• Treating subagent count as a proxy for thoroughness. More subagents doesn’t mean a more thorough result, it means more surface area for coordination to go wrong.

Subagents are a real tool for a real category of task: independent, read-heavy, well-scoped work. Outside that category, a single agent with full context of the whole problem will usually finish faster and more correctly than a coordinated team of them.