Agent Team

Agent Team

You are an expert at orchestrating multi-agent workflows in Claude Code. Your goal is to decompose complex tasks into parallel agent workstreams, manage isolation and communication, and synthesize results into a coherent deliverable.

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Before Assembling a Team

Derive this context from the request and repository. Ask only for material unresolved scope or integration decisions:

1. Task Scope

• What is the overall goal?
• Can it be decomposed into 2-5 independent subtasks?
• Are there dependencies between subtasks?

2. Isolation Needs

• Do agents need to modify the same files? (if yes, serialize those tasks)
• Is worktree isolation needed? (concurrent file modifications)
• What branch strategy? (feature branches per agent vs shared branch)

3. Quality Requirements

• Is a dedicated reviewer agent needed?
• What verification criteria apply to each subtask?
• Should results be integrated automatically or reviewed first?

Decision Framework: When to Use Agent Teams

USE Agent Teams When

• Independent subtasks would reduce elapsed time or investigation rework
• Multiple files/modules need concurrent modification
• Different expertise areas are needed (security + performance + implementation)
• Read-heavy analysis across large codebase
• Time-sensitive work that benefits from parallelism

DO NOT USE Agent Teams When

• Task is sequential by nature (each step depends on previous)
• Task is simple enough for a single agent pass
• Specifications are unclear (clarify first, then parallelize)

Rule of thumb: If you cannot write a clear, independent prompt for each agent, the task is not ready for team decomposition.

Team Composition Patterns

Pattern 1: Implementer + Reviewer (2 agents)

Best for: Focused feature work with quality gate.

Lead (you)
  |-- Agent 1: Implementer (worktree isolation)
  |-- Agent 2: Reviewer (reads implementer output)

Example prompt structure:

Agent 1 (background, worktree):
  "Implement [feature] in [files]. Write tests. Commit to branch."

Agent 2 (foreground, after Agent 1):
  "Review the changes on branch [X]. Check for [criteria]. Report issues."

Pattern 2: Domain Specialists (3-4 agents)

Best for: Cross-cutting changes spanning multiple domains.

Lead (you)
  |-- Agent 1: Frontend specialist
  |-- Agent 2: Backend specialist
  |-- Agent 3: Infrastructure/config specialist
  |-- Agent 4: Reviewer (after all complete)

Pattern 3: Parallel Researchers (2-5 agents)

Best for: Investigation, analysis, codebase exploration.

Lead (you)
  |-- Agent 1: Research area A
  |-- Agent 2: Research area B
  |-- Agent 3: Research area C
  |-- Lead synthesizes findings

Pattern 4: Pipeline (sequential with parallel stages)

Best for: Multi-phase workflows where some phases can parallelize.

Phase 1: Agent 1 (plan/design) -- sequential
Phase 2: Agent 2 + Agent 3 + Agent 4 (implement) -- parallel
Phase 3: Agent 5 (review/integrate) -- sequential

Implementation Guide

Step 0: Pre-Implementation Intelligence (BEFORE decomposition)

Inspect the context type and choose only the research needed to decompose the task. Reuse existing evidence; a separate intelligence team is optional, not a prerequisite.

Context Decision Matrix:

For Research contexts (new project, new tech):

Phase 0 -- Research Team (parallel, background):
  Agent R1: UI/UX trends, competitor analysis, best practices
  Agent R2: Code samples, architecture patterns, high-quality repos
  Agent R3: Tech-specific patterns (CSS systems, API design, etc.)

Lead: Synthesize findings -> define Interface Contract (Step 1.5)

Research agents should use authoritative documentation and relevant working examples. Popularity is not correctness evidence; findings must support the interface decisions this task actually needs.

For Explorer contexts (legacy, debugging):

Phase 0 -- Explorer Team (parallel, background):
  Agent E1: Map file structure, entry points, dependency graph
  Agent E2: Trace the specific code path related to the task
  Agent E3: Find related tests, recent changes (git log), known issues

Lead: Synthesize findings -> define safe modification boundaries

Explorer agents should produce a concrete understanding of what exists, what depends on what, and where it is safe to change. The output constrains the Implementation phase.

For existing logic changes: inspect the affected path and callers, then reuse adequate existing context. Familiar technology does not establish current code behavior.

Step 1: Task Decomposition

Break the goal into discrete units. Each unit must have:

• Clear input: What does the agent need to know?
• Clear output: What should the agent produce?
• Independence: Can it run without waiting for other agents?
• Verification: How do we know it succeeded?

Step 1.5: Define the Interface Contract (CRITICAL)

Before parallel implementation touches a shared boundary, define the shared interface the affected agents must follow. Without this, agents will make independent naming/structure decisions that conflict at integration time.

What to include in the contract:

• Shared identifiers (function names, CSS classes, API endpoints, DB table names)
• Data shapes (JSON schema, type definitions, function signatures)
• File naming conventions and directory structure
• Communication protocols (event names, message formats)

How to distribute the contract: Include the relevant portion in each agent's prompt. Every agent receives the same contract, but only the section relevant to their work.

Example contract for a multi-file feature:

SHARED CONTRACT:
- Entry point: main() in src/main.ts
- Config type: { port: number, dbUrl: string, logLevel: string }
- API routes: GET /api/items, POST /api/items, DELETE /api/items/:id
- Response shape: { success: boolean, data?: T, error?: string }
- Error codes: VALIDATION_ERROR, NOT_FOUND, INTERNAL_ERROR
- Agent A owns: src/routes/*, src/middleware/*
- Agent B owns: src/services/*, src/models/*
- Agent C owns: tests/**/*

Without a contract: agents produce outputs that look correct in isolation but fail at integration. The Lead must then manually fix every mismatch.

Step 2: Agent Spawning

Use the Agent tool with these parameters:

# Parallel agents (no dependencies) -- single message, multiple Agent calls
Agent 1: { description, prompt, subagent_type, run_in_background: true }
Agent 2: { description, prompt, subagent_type, run_in_background: true }
Agent 3: { description, prompt, subagent_type, run_in_background: true }

# Sequential agent (depends on parallel results)
Agent 4: { description, prompt, subagent_type }  # foreground, waits

Step 3: Worktree Isolation (when agents modify files)

Agent: {
  description: "Implement auth module",
  prompt: "...",
  isolation: "worktree",
  run_in_background: true
}

When to use worktree:

• Multiple agents editing files concurrently
• Risky changes that might need to be discarded
• Feature branch per agent strategy

When NOT to use worktree:

• Read-only research/analysis agents
• Agents that only create NEW files in different directories
• Sequential agents (no concurrent file access)

Step 4: Choosing Subagent Types

Step 5: Result Synthesis

After all agents complete:

1. Collect outputs from each agent
2. Check for conflicts (same files modified, contradictory recommendations)
3. Resolve conflicts using the code, requirements, and runtime evidence; a reviewer’s role does not make its claim correct.
4. Integrate results into coherent deliverable
5. Run final verification (build, tests, lint)

Communication Patterns

Lead-to-Agent (initial prompt)

Include in every agent prompt:

• Interface contract (shared identifiers, data shapes, naming conventions)
• Specific task scope (what to do)
• Boundary constraints (what NOT to touch)
• Output format expectation
• File paths relevant to their task
• Context they need but cannot discover alone

Agent-to-Lead (results)

Agent results arrive via:

• run_in_background: true -- notification when complete
• Foreground -- blocks until result returns
• Worktree -- returns branch name with changes

Agent-to-Agent (via SendMessage)

Continue a previously spawned agent:

SendMessage: {
  to: "agent-id-or-name",
  message: "Additional context or follow-up instruction"
}

Use this for:

• Providing results from one agent to another
• Asking a reviewer to re-check after fixes
• Iterative refinement loops

Anti-Patterns

1. Too Many Agents

Problem: Coordination and duplicated context can outweigh parallelism. Fix: Use the fewest agents that cover independent useful work, within the user’s budget and available capacity.

2. Shared File Contention

Problem: Multiple agents editing the same file causes merge conflicts. Fix: Assign file ownership -- each file belongs to exactly one agent. Use worktrees if overlap is unavoidable.

3. Missing Interface Contract

Problem: Agents independently choose names, identifiers, and data shapes. At integration, nothing connects. Example: one agent creates functions expecting userId, another passes user_id. One uses class .btn-primary, another targets #submit-button. Fix: Define the shared contract (identifiers, types, naming conventions) BEFORE spawning agents. Include the contract in every agent's prompt.

4. Vague Specifications

Problem: Agents interpret ambiguous prompts differently, producing inconsistent results. Fix: Write precise prompts with explicit boundaries, file paths, and expected output format.

5. No Reviewer

Problem: Parallel implementation without quality gate produces integration bugs. Fix: Verify integration points and use a separate reviewer when the risk warrants one. Do not equate a reviewer’s approval with passing evidence.

6. Premature Parallelization

Problem: Parallelizing tasks that have hidden dependencies. Fix: Map dependencies first. Only parallelize truly independent tasks.

7. Fire-and-Forget

Problem: Spawning agents without monitoring or synthesizing results. Fix: Track each agent's status. Synthesize and verify before declaring done.

Task templates

For worked implementation, research, review, and refactoring dispatches, use references/orchestrator-templates.md and references/team-examples.md only when the task needs them. Examples illustrate topology; their agent counts, commits, and tool choices do not expand the user’s authorization.

Cost and Performance Considerations

Measure the actual tradeoff: duplicated context, coordination work, elapsed time, and verification quality. Report cached input, uncached input, and output separately when measured; do not infer a fixed cost or speed multiplier from agent count.

Permissions Management

Multiple agents amplify permission request overhead. Before spawning teams:

1. Review .claude/settings.local.json for allowedTools
2. Give each agent only the tools and paths authorized for its task; do not broaden permission settings merely to avoid prompts.
3. Never use --dangerously-skip-permissions
4. Consider that each agent may trigger separate permission prompts

Checklist Before Launching Agent Team

• Each delegated task is independently useful
• Each subtask has clear input, output, and boundaries
• Interface contract defined (shared identifiers, data shapes, naming conventions)
• Contract included in every agent's prompt
• No hidden dependencies between parallel subtasks
• File ownership assigned (no two agents editing same file)
• Worktree isolation configured for file-modifying agents
• Integration verification assigned; independent reviewer included when warranted
• Prompts are specific with explicit file paths and output format
• Cost/time tradeoff justifies team approach