Claude Code Agent Teams, Explained

The short version

Agent Teams turns Claude Code into a multi-agent system.

You get a team lead coordinating multiple Claude Code instances, and teammates can message each other directly. That's a big shift from the typical "subagent" model where only the lead can talk.

If you ship software, this changes how you split work and how you keep large changes under control. For a deeper look at how multi-agent coordination patterns have evolved, see our multi-agent orchestration patterns overview.

What Agent Teams is

Agent Teams lets you run multiple Claude Code instances working in parallel.

The lead handles coordination and task allocation. Teammates take individual workstreams, and they can communicate directly with each other. This makes the team feel less like a hub‑and‑spoke model and more like an actual engineering team. Anthropic has designed this to mirror real‑world software teams.

How it works

Lead + teammates

The team lead is the orchestration layer. It assigns tasks, integrates feedback, and owns the final plan.

Teammates are autonomous Claude Code instances. They work independently, but can also message each other, which reduces coordination overhead.

Split-pane mode or in-process

You can run Agent Teams in split-pane mode (tmux or iTerm2), or in-process.

Split-pane mode gives you visibility into each agent's thinking. In-process is cleaner for production-like workflows where you want less UI noise.

Delegate mode

Delegate mode restricts the lead to coordination only.

This is a guardrail for complex changes. The lead can't modify code directly-it can only direct teammates and consolidate outputs. It's a useful control when you want structured execution.

Plan approval workflow

Agent Teams includes a plan approval workflow for risky tasks.

This helps teams insert human oversight at the right point: after planning, before execution. It's a good fit for high-risk changes like migrations or infra updates.

Why it matters for builders

Agent Teams is the first major step toward "team-scale" AI workflows.

Single-agent systems are strong, but real engineering work is multi-discipline. You need one agent on tests, one on docs, one on infra, and another on core code changes. Agent Teams maps to this reality. If you're already running multiple agents in production, the lessons from running 14 AI agents are directly relevant here.

Best-fit use cases

Research + review

You can assign one teammate to gather context and another to review edge cases.

The lead then stitches that into a plan. This reduces the blind spots that single-agent systems often have.

New features

Features typically require multiple layers: schema, API, UI, tests, docs.

Agent Teams can split these into parallel tasks. The lead keeps architecture consistent while the teammates execute.

Debugging hypotheses

A common debugging workflow is to test multiple hypotheses in parallel.

Agent Teams can spin up teammates to test different theories, then converge on the most likely root cause.

Cross-layer changes

Cross-layer work is painful because it needs coordination across multiple modules.

With Agent Teams, you can assign each layer to a teammate and keep the integration owned by the lead.

Agent Teams vs subagents

The main difference is communication.

Subagents are typically isolated. They report back to the lead, but they don't talk to each other. Agent Teams removes that friction: teammates can coordinate, which speeds up convergence and reduces duplicated work.

Another difference is control. With delegate mode and plan approvals, Agent Teams gives you formal guardrails that most subagent systems lack. See how Agent Teams compares with editor-native approaches in our Cursor vs Claude Code agentic teams comparison.

What this means for builders

Build workflows around coordination, not prompts

The key shift is from "prompt engineering" to "team design."

You'll get better results by defining roles, task boundaries, and communication patterns than by squeezing more detail into a single prompt.

Insert oversight at the plan stage

Plan approvals are the right place for human review.

In practice, this is where you catch risky approaches before execution. It's a higher-leverage checkpoint than reviewing code at the end.

Lean into parallelism

Agent Teams is best when tasks can run independently.

Break work into clean slices: docs vs tests vs implementation. The lead's job is to keep architecture and interface decisions consistent.

Practical setup tips

Start with a small team

Don't spin up five agents on day one.

Start with two teammates: one for analysis/research and one for execution. Add more only when you can maintain clear task boundaries.

Use delegate mode for risky changes

Delegate mode forces discipline.

For migrations, security work, or refactors, it keeps the lead focused on coordination and reduces the chance of hasty changes.

Treat messaging as a first-class tool

Direct teammate communication is the feature that makes this work.

Encourage teammates to share intermediate findings, not just final outputs. It keeps the lead informed and reduces rework.

Limitations and watch-outs

Operational overhead

More agents means more coordination.

If your tasks are simple, Agent Teams can be overkill. It's a better fit for complex work that naturally splits.

Human review still matters

Plan approvals are a good safeguard, but they don't replace review.

You should still audit changes, especially when the model is making wide-ranging edits.

Bottom line

Agent Teams is Anthropic's most practical multi-agent feature yet.

It aligns with real engineering workflows: parallel execution, communication between specialists, and structured oversight. For a real-world example of this in action, see our case study on overnight agent builds. If you've struggled to scale single-agent workflows, this is the feature that finally makes team-scale AI feel workable.