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Claude Code supports up to 10 parallel subagents; Cursor has cloud agents; Codex has codex cloud. Parallel agents are powerful and chaotic. Learn the coordination patterns that work and the failure modes that hurt.
Spawning multiple agents in parallel is the killer feature of 2026 coding tools. Claude Code can run up to 10 subagents at once. Cursor's cloud agents run in parallel branches. Codex CLI has `codex cloud` for background work. Done well, this collapses a day of work into an hour. Done badly, it's ten agents fighting for the same files.
| Task | Why parallel works |
|---|---|
| Codebase exploration | Each agent reads a different module — no overlap |
| Independent bug fixes | Different files, different test files, different PRs |
| Test generation across modules | Agents can each tackle one module's tests |
| Documentation generation | Each subagent documents one feature area |
| Migration sweeps | Each agent handles one batch of files for the same migration |
| Pattern | How it works | Best for |
|---|---|---|
| Branch-per-agent | Each subagent works on its own git branch; merge at end | Independent feature work, parallel bug fixes |
| Lane-per-agent | Each agent owns specific paths (paths/A/* vs paths/B/*) | Codebase exploration, parallel module work |
| Producer-consumer | Agent A produces a plan or spec; Agent B implements | Plan-then-execute workflows |
| Scout-and-builder | Scouts read and summarize; one builder writes code | Large unfamiliar codebases |
| Reviewer pair | Builder writes; reviewer (different model) audits | Quality-critical features |
# .claude/agents/scout.md --- name: scout description: Read-only codebase exploration tools: [Read, Grep, Glob] # NO write/edit tools --- Your job is to explore and summarize. Never edit files. Return a Markdown report with file:line references. # .claude/agents/builder.md --- name: builder description: Implementation tools: [Read, Edit, Write, Bash] --- Implement based on the plan provided. Run tests after each edit. Never edit files in test/ or migrations/. # .claude/agents/reviewer.md --- name: reviewer description: Read-only PR review tools: [Read, Bash] --- Review the diff. List bugs and risks. Never edit.Three subagents with different tool grants. Scout finds, builder builds, reviewer audits. Roles by permission, not by polite request.# In Claude Code main session: "Spawn a scout subagent. Have it explore src/auth/ and write a Markdown plan for the refactor we discussed. Wait for its report." # Once the plan exists: "Spawn a builder subagent for each of the 4 plan steps in parallel, with each one on its own branch named refactor/auth-step-1 through 4. Do not start step 2-4 until step 1's branch passes CI." # Then: "Spawn a reviewer subagent for each PR. Aggregate the findings into one report."Sequenced parallelism. Some steps fan out, some serialize. The orchestrator (you, or the main session) decides.Subagents in Claude Code don't share memory. They communicate via the orchestrator (the main session) or via files written to disk. A common pattern: each subagent writes a `.claude/scratch/<agent-name>.md` report; the orchestrator reads all of them and synthesizes. Files are the message bus.
Ten agents in parallel is ten interns who never sleep. Ship the standards, not the code.
— A staff engineer adopting subagents
The big idea: parallel agents are an organizational skill more than a technical one. Pick the coordination pattern that matches the work, scope each agent's tools and paths, branch aggressively, and serialize merges. Done well, you outpace the team. Done poorly, you create the kind of mess one engineer would never make alone.
8 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-coding-debug-multi-agent-coordination-creators
What is the main idea of "Multi-Agent Coordination — When Subagents Step on Each Other"?
Which concept is most central to "Multi-Agent Coordination — When Subagents Step on Each Other"?
Which use of AI fits this topic best?
What should a careful learner remember about "10 parallel agents = 10x token cost"?
You want to use AI after this lesson. What is the safest next step?
How should AI output about subagent be treated?
Name one way to verify an AI answer about subagent.
Which action would help you apply "Multi-Agent Coordination — When Subagents Step on Each Other" responsibly?