Phase 8: Multi-Agent Systems & Platform Architecture · 50 min · Claude Code · Python

Multi-Agent Production Patterns

A specialist agent team isn't five agents running whenever — it's a dependency graph, and running a step before its dependency finishes isn't parallelism, it's a race condition.

Real-world application: Scheduling a planner-coder-reviewer-tester-deployer pipeline against a real dependency graph — not just calling agents in a fixed sequence — is the concrete difference between a toy multi-agent demo and production orchestration.

What you will learn

Design a specialist agent team (planner, coder, reviewer, tester, deployer) with an explicit dependency graph between roles
Run independent specialist agents in parallel while strictly respecting dependencies between dependent ones
Explain why Anthropic's multi-agent research system has subagents return findings, not raw context, to the lead agent
Detect and reject a cyclic dependency in an agent task graph instead of allowing it to hang or misorder

Introduction

Multi-Agent Production Patterns

Objective

Schedule a planner-coder-reviewer-tester-deployer pipeline against a real dependency graph, running independent tasks concurrently, and detect a cyclic dependency instead of hanging.

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Key terms in this lesson

Agent