Subagents vs Separate Agents: Which Setup Wins?
TL;DR: A subagent is a helper your lead agent spawns mid-task when a detail needs exploring, so the main line never stalls. A separate agent is a fully independent session running side by side with others, with its own context, its own worktree, and its own review. Neither wins outright: the skill is knowing when to delegate and when to parallelize. Pipper Code, a free macOS and Windows desktop app for orchestrating many AI coding agents, supports both patterns from one interface.
What Are Subagents?
When teams first ask “what are subagents?” the answer is short: an agent running inside another agent’s run. Instead of you launching a second full session, a lead agent spawns the helper itself with a narrow charter — “trace this test failure,” “summarize this schema,” “check the accessibility of one component.” The helper receives a trimmed slice of the lead’s context and its own scope. When it finishes, the result returns to the lead, which folds it into the larger plan and keeps going.
That is agent delegation in its purest form: one agent hands a piece of its work to a helper instead of abandoning its current line of thought. The pattern shows up across Claude Code, Codex, Copilot, and OpenCode under names like task, thread, or subtask. Whatever the label, the mechanics are identical — parent spawns child, parent merges, and the main thread never shuts down.
What Are Separate Agents?
A separate agent is the opposite shape. It is a full, independent run with its own session, its own context window, and — if you set it up that way — its own worktree. Nothing spawns it and nothing owns it. You launch a Claude Code agent to refactor pricing at the same time you launch a Codex agent to audit the repository. Each keeps its own conversation history, each guards its own files, and the two only meet when you review their diffs together.
That is the subagent vs multi agent difference in one line: subagents are one agent’s internal workers; separate agents are parallel peers you orchestrate. For the mechanics of running several full agents side by side, our parallel agents guide walks the whole workflow.
Subagents vs Separate Agents: The Comparison
Parallelizing across independent sessions is the wide win — several jobs get real time. Delegating to a subagent is the deep win — one job keeps continuity without re-explaining itself. The table says when each is right:
| Subagents vs Separate Agents | Subagents (delegation) | Separate Agents (parallel) |
|---|---|---|
| Coordination | One lead agent owns the goal and merges results | Zero coordination by default; you orchestrate the handoffs |
| Cost | Low — a narrow prompt inherits the parent’s context | Linear — every full session pays for its own context window |
| Context | Inherits and trims the parent’s context; continuity kept | Each session holds only what you feed it; no shared memory |
| Isolation | Shared lineage; a bad subagent result lands on the lead | Full isolation — a failed run never touches the others |
| Best when | A long main task gets blocked by a small detail worth chasing | Truly independent chunks of work that can finish at once |
Read it diagonally and the verdict writes itself: delegation buys cheap context continuity; parallel agents buy isolation and real concurrency. They are complementary tools for different problems, not competitors for the same one.
Worked Example: Plan a Feature Two Ways
Watch the two setups play out on the same task — adding a promo-code field and validation to a checkout page.
One lead agent plus subagents. A single Claude Code agent owns the feature. It starts by mapping the checkout component, then realizes it needs the promo table’s schema before it can design the field. It spawns a subagent to read the migration files and report the schema while the lead keeps sketching CheckoutForm.tsx. The report returns, the lead finishes the form, spawns a second subagent to run the test suite on the diff, and hands you one coherent change. No context was re-explained, no thread was diverted, and you review a single plan.
Two independent agents. You launch two ACP agents into one scene: a Claude Code run on the frontend worktree adding the field, and a Codex run on the backend worktree adding promo validation, the discount endpoint, and a test. Both stream into the same window and finish at roughly the same wall-clock time. The cost: the frontend agent may rename an API parameter mid-run that the backend agent has already coded against — a split you only discover at the merge gate.
Both paths work. A lead with subagents costs far fewer tokens and keeps a continuous context; two separate agents finish in genuine parallel time and deliver whole halves of the stack at once. For a small, tightly coupled feature, delegate. For wide, loosely coupled work, parallelize.
When to Delegate, When to Parallelize
A generic dos-and-don’ts frame, from both angles:
- Reach for subagents when one long task hits a detail: a stack trace, a question, a style audit, a single test failure. The support for spare delegation keeps the main line warm and close to free.
- Reach for separate agents when the work genuinely splits — backend and frontend, audit and implementation, two repos. Each agent gets its own worktree and its own context, and the wall-clock win outvalues the coordination effort.
- Avoid subagent nesting traps. A lead that spawns a subagent for every small spot-check can spend more tokens on the ceremony than on the work. Keep subagents thin and disposable, and delete them after the merge.
- Avoid parallel collisions. Two agents editing the same file is the classic clobber. The workaround is mechanical: one worktree per separate agent, and conflicts can only happen at the review gate you control.
How Pipper Code Supports Both
Pipper Code is a free desktop app (macOS and Windows) that runs and orchestrates many AI agent — Claude Code, Codex, Cursor, OpenCode, Copilot, Grok — from one interface. Because it is built on the Agent Client Protocol (ACP), you bring your own agents, and both structural choices are native:
- Delegate. Spawn a subagent from any running thread; it inherits the parent’s context as its own line while the parent keeps executing. Watch both in the same monitor pane and approve before anything merges.
- Parallelize. Launch two agents into one scene, each into its own worktree, and review both diffs in one window instead of five terminals.
# Two independent agents in one scene, each on its own worktree
$ pipper agent launch claude-code --workspace checkout/frontend --prompt "Add the promo-code field to CheckoutForm.tsx"
$ pipper agent launch codex --workspace checkout/backend --prompt "Add promo-code validation and the discount endpoint"
# A subagent from either thread when a detail appears
$ pipper subagent spawn --parent codex --prompt "Why does validate_discount() throw on empty codes?"Being able to pick delegation or parallelism per moment is what “AI Agent Orchestration” is really about — and for wiring in agents you already run elsewhere, see Bring Your Own Agents.
Key Takeaways
- A subagent is a worker a lead agent spawns mid-run to chase a detail; a separate agent is a fully independent session you orchestrate.
- Delegation preserves one continuous context at low token cost; parallel agents deliver isolation and true concurrency.
- Choose by task shape: delegate when a main line is blocked on a detail, parallelize when pieces are genuinely independent.
- Isolation solves clobbering — one worktree per agent, and collisions only happen at your review gate.
- Pipper Code supports both moves in one interface, free, on macOS and Windows.
FAQ
What Are Subagents?
Subagents are compact worker passes an agent spawns mid-run to handle a specific sub-task — run one test, read a schema, draft a script. They get a trimmed slice of the parent’s context and return a result for the lead to merge, so the main conversation never stalls on detail work.
What Is the Difference Between Subagents and Separate Agents?
A subagent is owned by a lead agent and inherits its lineage and message; a separate agent is an independent session with its own context, its own conversation, and typically its own worktree. Subagents add depth to one continuous plan; separate agents add parallel width across many sessions.
Do Separate Agents Cost More Than Subagents?
Usually, yes. Every separate agent pays for a truly relevant context window, while a subagent runs narrow against the parent’s context that is already loaded. The cost win flips if you build long subagent chains for trivial work — the honest measure is whether the delegation ceremony costs more than the task.
Which Setup Is Better for Multi-Agent Coding?
The short answer is both. Delegate small rabbit holes to keep your main line steady, and run independent agents in parallel when the work splits. Tools like Pipper Code give you both from one interface, so the structure follows the task instead of the task bending to a fixed workflow.
Conclusion
Subagents vs separate agents is less a war and more two hands of one job. Delegation keeps one deep thread warm and cheap when work should stay in one continuous context; separate agents give you genuine parallel throughput when the work splits wide. The earned skill is reading the task’s shape and matching the pattern to it.
Pipper Code gives you both without switching tools: spawn subagents mid-run, run multiple agents side by side in separate worktrees, and review everything from one window.
Download Pipper Code free and run your next feature both ways: /download.