Agent Teams

Agent Teams

A Claude Code multi-agent mode where agents share a task list, can communicate directly with each other, and can assign work to one another — an extension of sub-agents with peer-to-peer coordination.

Key points

  • Sub-agents run in parallel with fresh context but can only report back to the main thread — no peer communication [src-011]
  • Agent teams: all agents share a task list and can communicate peer-to-peer, not just top-down [src-011]
  • Individual agents in a team can be addressed directly by the user, not just through the main orchestrator [src-011]
  • More expensive and longer-running than sub-agents, but produce more cohesive outputs on large, interdependent projects [src-011]
  • Best used for complex projects requiring cross-component coherence; sub-agents better for isolated parallel tasks [src-011]
  • The 10-hour course teaches agent teams as part of the advanced Claude Code layer after skills and sub-agents, alongside browser automation, permissions, context management, and worktrees [src-016]
  • Boris Cherny describes massive parallelism as an active frontier: his own setup often has hundreds of agents and overnight thousands of agents, while the product team is working on easier multi-agent delegation, batch, loop, and team patterns [src-054]
  • As models improve, Boris expects the model to infer more of when to parallelize work instead of requiring users to manually decide where to spawn sub-agents [src-054]
  • Cursor's team-era framing converges on the same pattern from an IDE/product angle: agents get their own remote computer environments, run for hours or days, and let humans supervise many parallel implementation streams [src-080]
  • Cursor's autonomous-browser experiment is an extreme version: an agent team worked over roughly a week, generated millions of lines, and produced a mostly functional prototype browser, though Cursor emphasizes this remains experimental [src-080]

When to use

Scenario Use
Independent parallel tasks (e.g., 5 separate summaries) Sub-agents
Complex projects with interdependent components (e.g., full-stack app with API + frontend + tests) Agent teams
Single isolated tasks Main thread

Related entities

Related concepts

Source references

  • [src-011] Nate Herk — Claude Code power features cluster (2026-04-20 to 2026-04-27)
  • [src-016] Nate Herk — "Build & Sell with Claude Code (10+ Hour Course)" (2026-03-12)
  • [src-054] Sequoia Capital — "Anthropic's Boris Cherny: Why Coding Is Solved, and What Comes Next" (2026-05-04)
  • [src-080] Cursor — "The next era of AI coding" (2026-05-12)

Robin Cartier perspective

This page is part of Robin Cartier's working AI knowledge graph: a practical research layer for production AI, recommendation systems, experimentation, GEO, and agentic web readiness.

The useful next step is to connect this concept back to applied product leadership and operating models.

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