Claude Code Agent Teams: A Complete Guide to Advancing Multi-Agent Development

Claude Code has introduced a new feature called Agent Teams, designed to enable multiple agents to operate simultaneously within a single Claude Code session. Each agent is assigned a distinct role, allowing them to collaborate, share context, and work in parallel on complex tasks.

While this may sound like a major conceptual leap, the underlying idea is not entirely new.

The OpenClaw community had already been experimenting with a similar approach - coordinating multiple Claude Code sessions in parallel, each handling a specialized responsibility. What was once a community-driven workaround has now been formally integrated into Claude Code as a native feature, requiring no plugins or custom extensions.

From OpenClaw Experiment to Official Feature

Within the OpenClaw community, users had long recognized the limitations of running large or complex tasks through a single agent. Assigning research, architecture design, debugging, and validation to one agent often resulted in linear exploration, delayed feedback loops, and costly rework when early assumptions proved incorrect.

To address this, practitioners began running multiple Claude Code sessions concurrently. Each session acted as a specialist, working independently while periodically exchanging findings. This model proved to be both practical and repeatable in real development environments.

Agent Teams represents Anthropic’s formal adoption of this pattern. The same idea - parallel agents with defined responsibilities - has now been embedded directly into Claude Code under an official, supported interface.

Claude Code Before Agent Teams

Prior to Agent Teams, Claude Code functioned as a highly capable but fundamentally individual contributor.

A typical workflow followed a sequential pattern:

Research → Implementation → Revision → Testing

This approach was straightforward and effective for smaller tasks. However, it had inherent structural limitations:

• Alternative perspectives were difficult to introduce mid-process
• Incorrect assumptions often required restarting from earlier steps
• Tasks that could be parallelized were forced into a serial flow

Agent Teams changes this model at a foundational level.

How the Agent Teams Model Works

When an Agent Team is created, Claude Code first initializes a lead agent. The lead agent is responsible for understanding the overall task, breaking it down into components, and coordinating execution.

For each component, a teammate agent is spawned. Each teammate runs as an independent Claude Code session with its own context window and working memory.

These agents are not subordinate utilities. Each teammate:

• Maintains an independent context
• Operates in its own workspace
• Can communicate directly with other agents

Rather than branching thoughts within a single agent, Agent Teams creates multiple concurrent reasoning entities. The lead agent oversees progress, integrates outputs, resolves conflicts, and adjusts direction when necessary.

How Agent Teams Differs from Sub-Agents

Experienced Claude Code users may already be familiar with sub-agents.

Sub-agents are well suited for short, scoped tasks such as gathering references or summarizing a narrow topic. They return results to the main agent and are typically discarded afterward.

Agent Teams operates on a different principle.

Teammate agents persist as role-specific specialists for the duration of the task. Communication is not limited to reporting upward to the lead agent; teammates can question, validate, and refine each other’s work directly.

This results in tangible benefits:

• Reduced perspective bias
• Faster hypothesis testing
• Meaningful parallel execution

Where Agent Teams Are Most Effective

Agent Teams is not universally optimal. Its strength lies in tasks that can be decomposed into semi-independent units.

Common high-impact use cases include:

• Technical research or competitive analysis from multiple viewpoints
• New feature development with clear module or layer boundaries
• Debugging complex issues using parallel hypotheses
• Separating frontend, backend, and testing responsibilities

Conversely, tasks that are strictly sequential or require frequent edits to the same files may experience reduced efficiency. Evaluating whether parallelization adds value is a critical first step.

Enabling Agent Teams

Agent Teams is currently an experimental feature and must be enabled manually.

The most straightforward approach is to add the following configuration to settings.json:

Code
{
  "env": {
    "CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS": "1"
  }
}

While environment variables can also be used, storing the setting in the configuration file improves reuse and consistency across sessions.

Once enabled, Claude Code will recognize instructions related to agent team creation and management.

Creating Your First Agent Team

No special syntax is required. You can request an agent team using natural language.

What matters most is clarity of roles. Explicitly defining perspectives or responsibilities - such as UX evaluation, technical architecture, or critical review - allows the lead agent to decompose tasks more effectively.

Ambiguous instructions force the lead agent to infer structure, increasing overhead and token usage.

Managing an Active Team

Once the team is running, you can interact with agents in several ways.

Teammate agents can be addressed directly without routing messages through the lead. This enables targeted clarification or redirection.

Using delegate mode, the lead agent can be restricted to coordination duties, preventing it from executing tasks directly. This keeps responsibility boundaries clear.

Tasks may be auto-assigned by the lead or manually delegated. After completion, teammate agents can be safely shut down, followed by a final cleanup and consolidation step performed by the lead.

Practical Token Optimization Strategies

Agent Teams inherently increases token usage.

Because teammate agents do not inherit the full conversation history, essential context must be included at creation time. Failing to do so often leads to repeated explanations and unnecessary overhead.

Task sizing also matters. Units that are too small incur coordination costs, while overly large tasks increase the risk of misalignment. Defining deliverables with clear scope and output expectations yields the best results.

Current Limitations

As a research preview, Agent Teams comes with several constraints:

• Teammate agents are not restored when sessions are resumed
• Task completion status may lag behind actual progress
• Only one agent team can exist per session
• Split-pane visualization is limited to tmux or iTerm2

With an understanding of these constraints, most users can work around them without major friction.

Looking Ahead

Agent Teams represents a shift in how Claude Code is positioned - from a powerful individual tool to a collaborative execution environment.

While the feature is still evolving, its direction is clear. Developers and researchers who begin using Agent Teams now will be well positioned to take advantage of future improvements and eventual full release.

For workflows involving complex reasoning, parallel exploration, or structured collaboration, Agent Teams is likely to become a core component of advanced Claude Code usage.