Multi-Agent Workflows with Claude Code: The Complete Guide

A single Claude Code agent is powerful. Multiple orchestrated agents? That's a complete development team. This guide explores multi-agent orchestration patterns.

Why Multi-Agent?

Modern projects involve parallelizable tasks:

Frontend and backend simultaneously
Tests and documentation in parallel
Refactoring multiple independent modules

A single agent handles these sequentially. Multiple agents handle them in parallel.

Orchestration Skills

Loki Mode — The Autonomous Approach

Score: 85/100 | The most ambitious

Loki Mode takes a PRD (Product Requirements Document) and deploys the product with minimal intervention. It:

Analyzes the PRD and breaks it into tasks
Creates specialized agents
Coordinates execution
Manages task dependencies
Deploys the result

When to use: Greenfield projects, prototypes, hackathons.

Prerequisite: The --dangerously-skip-permissions flag is required (Loki Mode needs full autonomy).

Gastown — The Modular Orchestrator

Score: 75/100 | The most flexible

Gastown uses unique vocabulary (convoys, polecats, rigs) to coordinate agents:

Convoys: Groups of related tasks
Polecats: Specialized agents
Rigs: Environment configurations
Beads: Atomic work units

When to use: Complex existing projects requiring fine-grained orchestration.

Orchestration Patterns

Pattern 1: Fan-Out / Fan-In

Orchestrator
  ├── Frontend Agent (React components)
  ├── Backend Agent (API endpoints)
  ├── Test Agent (E2E + unit)
  └── Docs Agent (README + API docs)

Each agent works independently, the orchestrator aggregates results.

Pattern 2: Sequential Pipeline

Analysis Agent → Implementation Agent → Review Agent → Deploy Agent

Each agent passes context to the next.

Pattern 3: Specialization

"Senior Dev" Agent (architecture, code review)
"Junior Dev" Agent (implementation, tests)
"DevOps" Agent (CI/CD, deployment)
"PM" Agent (specs, documentation)

Each agent has different roles and skills.

Putting It in Practice

Step 1: Identify Parallelizable Tasks

Find tasks without mutual dependencies. Example for a CRUD feature:

✅ Parallelizable: DB schema + UI components + tests
❌ Sequential: DB migration → API endpoint → frontend integration

Step 2: Configure Agents

Each agent needs:

An isolated working directory (git worktree)
Appropriate skills for its task
Clear context (what to do, boundaries)

Step 3: Manage Conflicts

The main multi-agent risk: merge conflicts.

Solutions:

Separate working directories (worktrees)
Split by file/module (no overlap)
Frequent merges to main branch

Limitations and Cautions

Cost: Each agent consumes tokens. 4 agents = ~4x the cost.
Complexity: Orchestration adds complexity. Don't multi-agent a simple project.
Conflicts: Agents can produce incompatible code. Reviews are essential.
Debugging: Tracing a bug across multiple agents is harder.

When to Use Multi-Agent?

Situation	Approach
Simple feature	1 agent is enough
Complex feature, independent modules	Multi-agent
Quick prototype	Loki Mode
Existing project, large refactoring	Gastown
Team wanting to standardize	Skills + 1 agent

Conclusion

Multi-agent is a force multiplier, not a silver bullet. Use it when tasks are truly parallelizable and the time savings justify the added complexity.