Claude Code Agent Teams: 10+ Use Cases with Copy-Paste Prompts

Problem: You have Claude Code Agent Teams enabled and running. But "create a team to help with my project" produces unfocused results. The difference between a productive team and a token-burning mess comes down to how you structure the prompt. These agent teams examples and prompt templates give you tested starting points for every common multi-agent workflow.

Quick Win: Try the parallel code review prompt below. It is the most universally useful Agent Teams pattern and works on any codebase:

Create an agent team to review PR #142. Spawn three reviewers:
- One focused on security implications
- One checking performance impact
- One validating test coverage
Have them each review and report findings. Use delegate mode so the
lead synthesizes a final review without doing its own analysis.

Three reviewers, three lenses, one comprehensive review. You will see results in minutes that would take a single reviewer three separate passes.

This is a companion guide to the Agent Teams overview. Start there if you have not set up your first team yet. For controls and configuration, see Advanced Controls.

Developer Agent Teams Use Cases

These prompts target the most common development workflows where parallel execution with active coordination outperforms sequential work.

1. Parallel Code Review

Create an agent team to review PR #142. Spawn three reviewers:
- One focused on security implications
- One checking performance impact
- One validating test coverage
Have them each review and report findings. Use delegate mode so the
lead synthesizes a final review without doing its own analysis.

Why it works: A single reviewer gravitates toward one type of issue at a time. Splitting review criteria into independent domains means security, performance, and test coverage all get thorough attention simultaneously. The lead synthesizes findings into a comprehensive review that catches issues a single reviewer would miss. In testing, three-reviewer teams consistently surfaced issues that single-pass reviews missed. Expect roughly 2-3x the token usage of a single-session review, a worthwhile trade for the coverage.

Delegate mode is important here. Without it, the lead tends to do its own review and then awkwardly merge it with the teammates' results. With delegate mode, the lead focuses entirely on coordination and synthesis.

2. Debugging with Competing Hypotheses

Users report the app exits after one message instead of staying connected.
Spawn 5 agent teammates to investigate different hypotheses. Have them talk
to each other to try to disprove each other's theories, like a scientific
debate. Update the findings doc with whatever consensus emerges.

Why it works: The debate structure fights anchoring bias. Sequential investigation suffers from it: once one theory is explored, subsequent investigation is biased toward confirming it. Multiple independent investigators actively trying to disprove each other means the surviving theory is more likely the actual root cause.

This pattern also surfaces unexpected connections. When teammate #3 finds a memory leak and teammate #1 was investigating timeout behavior, they can connect the dots directly without the lead acting as intermediary. That direct communication is what separates Agent Teams from subagent patterns.

3. Full-Stack Feature Implementation

Create an agent team to implement the user notifications system.
Spawn four teammates:
- Backend: Create the notification service, database schema, and API endpoints
- Frontend: Build the notification bell component, dropdown, and read/unread states
- Tests: Write integration tests for the full notification flow
- Docs: Update the API documentation and add usage examples

Assign each teammate clear file boundaries. Backend owns src/api/notifications/
and src/db/migrations/. Frontend owns src/components/notifications/.
Tests own tests/notifications/. No file overlap.

Why it works: File-level boundaries prevent merge conflicts. Each teammate knows exactly which directories they own, and the shared task list keeps everyone synchronized on progress. When the backend teammate finishes the API contract, the frontend teammate can pick it up immediately because they are both watching the same task list.

Without explicit file boundaries, two teammates will inevitably edit the same file and create conflicts. Directory-level ownership is the single most important detail in implementation prompts.

4. Architecture Decision Record

Create an agent team to evaluate database options for our new analytics feature.
Spawn three teammates, each advocating for a different approach:
- Teammate 1: Argue for PostgreSQL with materialized views
- Teammate 2: Argue for ClickHouse as a dedicated analytics store
- Teammate 3: Argue for keeping everything in the existing MongoDB

Have them challenge each other's arguments. Focus on: query performance
at 10M+ rows, operational complexity, migration effort, and cost.
The lead should synthesize a decision document with the strongest arguments
from each side.

Why it works: This deliberation pattern produces better architectural decisions than a single agent weighing options alone. Each teammate commits fully to their position and looks for weaknesses in the others. The lead synthesizes only the arguments that survive challenge.

I have found this especially useful for decisions where every option has real trade-offs and no clear winner. A single session tends to pick one early and rationalize it. The adversarial structure forces genuine evaluation of alternatives.

5. Bottleneck Analysis

Create an agent team to identify performance bottlenecks in the application.
Spawn three teammates:
- One profiling API response times across all endpoints
- One analyzing database query performance and indexing
- One reviewing frontend bundle size and rendering performance

Have them share findings when they discover something that affects
another teammate's domain (e.g., slow API caused by missing DB index).

Why it works: Cross-domain communication is where Agent Teams shine over subagents. When the database analyst discovers a missing index that explains the API teammate's slow endpoint, they can share that finding directly. This is the kind of collaboration that subagents simply cannot do, since subagents only report results back to the main session and never talk to each other.

The performance bottleneck pattern also benefits from the shared task list. As each teammate identifies issues, they log them to the task list with severity ratings. The lead can watch the picture form in real time and redirect effort toward the most impactful findings.

6. Inventory Classification

Create an agent team to classify our product catalog. We have 500 items
that need categorization, tagging, and description updates.
Spawn 4 teammates, each handling a segment:
- Teammate 1: Items 1-125
- Teammate 2: Items 126-250
- Teammate 3: Items 251-375
- Teammate 4: Items 376-500

Use the classification schema in docs/taxonomy.md. Have teammates
flag edge cases for the lead to review.

Why it works: Data-parallel work scales linearly with teammates. Each works through their segment independently, flagging ambiguous items for human review. Four teammates processing 125 items each finishes roughly 4x faster than a single session processing 500.

This pattern applies to any bulk operation: tagging support tickets, categorizing documentation pages, normalizing database records, or processing CSV files. The key is splitting the work by data boundaries, not by function.

Marketing and Non-Dev Agent Teams Use Cases

Agent Teams are not limited to code. Any task that benefits from parallel perspectives and active coordination works. These prompts demonstrate workflows for research, content, and campaign strategy.

7. Campaign Research Sprint

Create an agent team to research the launch strategy for [product].
Spawn three teammates:
- Competitor analyst: study competitor ad copy, positioning, and pricing
- Voice of customer researcher: mine reviews, Reddit threads, and forums
  for pain points and language customers actually use
- Positioning stress-tester: take findings from both teammates and
  pressure-test our current positioning against what they discover

Have them share findings and challenge each other. The lead synthesizes
a strategy document with positioning recommendations.

Why it works: The competitor researcher finds gaps in the market. The voice-of-customer teammate validates whether real buyers actually care about those gaps. The positioning stress-tester takes both inputs and tests whether your message holds up. Three lenses, one synthesis. Each teammate's output directly feeds the others.

Compare this to running three separate research sessions. You would get three independent reports and then spend time manually cross-referencing them. With Agent Teams, the cross-referencing happens automatically through inter-agent messaging.

8. Landing Page Build with Adversarial Review

Create an agent team to build the landing page for [offer].
Spawn three teammates:
- Copywriter: develop messaging, headlines, and body copy
- CRO specialist: design conversion structure, CTA placement, and flow
- Skeptical buyer: review everything as a resistant prospect, flag
  weak claims, missing proof, and friction points

Require plan approval before any implementation.

Why it works: The plan approval step catches bad directions before they burn cycles. The adversarial reviewer finds the holes that the builder-focused teammates miss. Real buyers are skeptical. Your team should be too.

Plan approval is especially important here because landing page copy is expensive to rewrite. Catching a weak value proposition at the outline stage takes minutes. Catching it after a full page build takes hours.

9. Ad Creative Exploration

Spawn 4 teammates to explore different hook angles for [product].
Each teammate develops one direction with headline variations,
supporting copy, and a rationale for why the angle works.
Have them debate which direction is strongest.
Update findings doc with consensus and runner-up options.

Why it works: One agent exploring alone anchors on the first decent idea. Four agents actively trying to outperform each other produces battle-tested creative. The debate structure means the winning angle survived real challenge, not just a single session's internal monologue.

I have seen this produce angles that no single session would have explored. When teammate #2 pushes back on teammate #1's approach, teammate #1 often refines their angle into something stronger rather than abandoning it. The competitive pressure raises the quality floor.

10. Content Production Pipeline

Create a team for this week's content calendar.
Spawn three teammates:
- Researcher: identify search intent gaps and competitive opportunities
- Writer: draft content based on research findings
- Quality reviewer: run each piece through clarity, proof, and SEO checks

Chain tasks so the researcher finishes before the writer starts,
and the reviewer checks each piece before marking it complete.

Why it works: Parallel research and sequential quality gates. The researcher and writer can overlap on different pieces while the reviewer catches issues before anything ships. Built-in QA without a separate review process.

Task chaining is the key detail here. Without it, all three teammates start simultaneously and the writer drafts content without research to draw from. Explicit task dependencies through the shared task list enforce the right execution order. For more on chaining tasks across agents, see async workflows.

Getting Started: A Progressive Path

If you are new to Agent Teams, start simple and build up. Jumping straight into a five-teammate implementation prompt is a recipe for confusion. This three-week progression builds your intuition for when teams add value and when they add overhead.

Week 1: Research and Review

Pick a PR that needs review. Enable Agent Teams, then run:

Create an agent team to review PR #142. Spawn three reviewers:
- One focused on security implications
- One checking performance impact
- One validating test coverage
Have them each review and report findings.

Three reviewers, three lenses, one comprehensive review. You will see how teammates work through the task list, communicate findings, and deliver results. Low risk, high learning. If something goes wrong, the worst case is an incomplete review that you can finish manually.

Week 2: Debugging with Debate

Take a bug report and use the competing hypotheses pattern:

Users report intermittent 500 errors on the checkout endpoint.
Spawn 3 teammates to investigate different hypotheses:
- One checking database connection pooling
- One investigating race conditions in the payment flow
- One analyzing server resource limits
Have them share findings and challenge each other's theories.

This teaches you how inter-agent communication works in practice. Watch how teammates share evidence, how they challenge weak theories, and how consensus forms. The shared task list is where most of this coordination becomes visible.

Week 3: Implementation

Once you are comfortable with coordination patterns, try a feature implementation with clear file boundaries:

Create an agent team to build the webhook system.
Assign directory-level ownership to prevent conflicts.
Use delegate mode for the lead.

By week three, you will have intuition for when teams add value and when a single session or subagent approach is the better choice. Most developers find that teams work best for tasks requiring three or more independent work streams with at least some cross-domain communication need.

Tips for Writing Better Team Prompts

After running dozens of Agent Team sessions, these patterns consistently produce better results:

• Be specific about roles: "one on security, one on performance" beats "reviewers." Vague roles produce vague work.
• Define file boundaries: Directory-level ownership prevents merge conflicts. This is non-negotiable for implementation tasks.
• Include success criteria: "Report findings" or "update the decision doc" gives each teammate a clear finish line.
• Use delegate mode for pure coordination: Keeps the lead from doing the work itself. The lead's job is synthesis, not production.
• Require plan approval for risky work: Catches bad directions before they waste tokens. Especially important for creative and implementation tasks.
• Let teammates argue: The friction produces better results than agreement. Debate patterns consistently outperform consensus-seeking patterns.
• Keep team size to 3-5: More teammates means more coordination overhead and higher token costs. Beyond five, the communication volume often outweighs the parallelism benefit.
• Match the pattern to the task: Data-parallel work (classification, processing) splits by data boundaries. Functional work (feature implementation) splits by domain. Evaluative work (architecture decisions, creative) splits by perspective.
• Speed up the lead with fast mode: Enable fast mode on the lead for snappier coordination while teammates run at standard speed to keep costs down.

For best practices, troubleshooting, and known limitations, see Agent Teams Best Practices. For display modes, token cost management, and quality gate hooks, see Advanced Controls.

Scaling Beyond Prompts

These prompts work out of the box for any Claude Code user with Agent Teams enabled. As your team workflows become more complex, you may want structured orchestration that handles agent routing, permission management, and coordination protocols automatically.

ClaudeFast's multi-agent system provides pre-configured agent definitions and invocation protocols for exactly this purpose. If you find yourself repeatedly setting up the same team structures, a framework that codifies those patterns saves significant setup time on every session.

The developers building agent team muscle memory today are investing in a skill that will compound as multi-agent AI tooling matures. Start with the code review prompt this week. The overhead is low, and the prompts in this guide give you a tested starting point for every common workflow.