smoke.sh — tiny post-deploy checks

9.1 Multi-Agent and Subagent Workflows

As software projects grow, a single AI assistant can only handle so much context before efficiency drops or responses become inconsistent. This is where multi-agent and subagent workflows come in—a system where multiple specialized Claude instances collaborate to complete complex development tasks. Instead of one Claude handling everything from documentation to deployment, you orchestrate a team of Claude-powered agents, each with a distinct role: a Planner for task decomposition, a Coder for implementation, a Reviewer for QA, and a Deployer for final rollout.

This chapter marks a major shift in your Claude usage—from using Claude as an individual assistant to building a team of coordinated AIs that think, communicate, and execute in parallel. By the end of this project, you’ll have a functioning Claude-based system capable of automating a software sprint: dividing work, writing code, testing, and merging—all under your supervision.

Concept Development

A multi-agent workflow mimics human team dynamics. Each agent specializes in one domain but collaborates through structured prompts and shared context. Claude’s subagents—spawned as separate, goal-driven processes—make this possible while maintaining isolation and focus.

For example:

• Planner Claude interprets user intent and creates a task breakdown.
• Coder Claude writes functional code for each task.
• Reviewer Claude checks logic, readability, and security.
• Deployer Claude runs tests, builds artifacts, and performs CI/CD actions.

By connecting these agents with clearly defined inputs and outputs, you achieve asynchronous or sequential collaboration similar to real-world DevOps teams. The key principle is context synchronization: each agent receives the relevant subset of project data without flooding Claude’s context window.

Claude Code supports such structured cooperation through prompt chaining and shared memory constructs, allowing you to design systems where each AI instance feeds the next intelligently.

Hands-On Example: Creating a Multi-Agent Workflow in Python

Let’s create a simple prototype that simulates Claude subagent collaboration using a clean and runnable Python script. In this scenario, the main agent delegates coding and review tasks to subagents and merges their outputs into a single deliverable.

from typing import Dict, Any

class ClaudeAgent:

    def __init__(self, name: str):

        self.name = name

    def respond(self, message: str) -> str:

        # In production, this would call Claude's API.

        print(f"[{self.name}] received: {message}")

        if "plan" in message.lower():

            return "1. Write API routes\n2. Add validation\n3. Implement tests"

        elif "implement" in message.lower():

            return "Code snippet: def create_task(): pass"

        elif "review" in message.lower():

            return "The code passes structure and naming standards."

        else:

            return "Acknowledged."

# Instantiate subagents
planner = ClaudeAgent("Planner")

coder = ClaudeAgent("Coder")

reviewer = ClaudeAgent("Reviewer")