Instance Management Patterns

Running one Claude Code instance is productive. Running two to four instances simultaneously is a step function in throughput. The principle is straightforward: while Instance A is processing a complex refactor (which takes 2-5 minutes of compute), you are building context and issuing prompts in Instance B. While Instance B processes, you review Instance A's output. The dead time between prompt and response — the time you would normally spend waiting — becomes productive work in another instance.

The optimal number of parallel instances depends on task complexity and your context-switching ability. Two instances is the sweet spot for most developers — enough parallelism to eliminate idle time without the cognitive overhead of tracking too many threads. Three instances works well when two tasks are in "processing" mode and one is in "review" mode. Four instances is the practical maximum for a single developer — beyond four, the overhead of tracking which instance is doing what exceeds the productivity gain from parallelism.

Task decomposition is the prerequisite for effective parallelism. You cannot parallelize a single sequential task — you must decompose the work into independent units that do not require coordination between instances. Good candidates for parallelism: feature implementation in Instance A while writing tests in Instance B. Refactoring module X in Instance A while adding a new endpoint in Instance B. Fixing a bug in Instance A while researching an architecture decision in Instance B.

Bad candidates for parallelism: two instances editing the same file, two instances that depend on each other's output, or two instances working on tightly coupled subsystems where a change in one invalidates assumptions in the other. The independence criterion is strict — if Instance A's changes could break Instance B's assumptions, they should be sequential, not parallel.