Multi-Agent Systems

Multi-Agent Systems are an architecture where multiple AI Agents divide work and collaborate to complete complex tasks. Compared to single Agents, multi-agent systems overcome three key limitations:

Limitation 1: Context Window ceiling. At 200K tokens, even Claude's large context window isn't enough for extremely large tasks (analyzing 5 years of company financials, comprehensive refactoring of entire large codebases). Multi-agent systems assign different Agents to different portions and integrate results — handling tasks far exceeding any single Context Window.

Limitation 2: Serial execution. Single Agents are sequential. Multi-agent systems run multiple Agents simultaneously, compressing originally 10-minute serial tasks to 2 minutes.

Limitation 3: Specialization depth. One "all-purpose" Agent is often superficial everywhere. Multi-agent systems let each Agent focus on one domain with targeted System Prompts and tools — output quality higher than a generalist Agent. Separating "code generation Agent" and "code review Agent" outperforms one Agent that both writes and reviews.

AnthropicClaude defines multi-agent systems in Anthropic Academy as one of the most important frontier directions in current AI development, providing native multi-agent architecture support in Claude Code and API (Subagents functionality).

What scenarios suit each of the three core multi-agent architecture patterns?

Pattern 1: Orchestrator-Worker

One "master Agent" (Orchestrator) analyzes the task, assigns subtasks, collects results, integrates output; multiple "executor Agents" (Workers) each execute their assigned subtask.

Suited for: tasks with clear decomposition structure where each subtask is relatively independent. Example: simultaneously analyzing annual reports of competitors A, B, C — Orchestrator assigns three Workers each to analyze one company, integrates into a comparison report.

Pattern 2: Pipeline / Sequential

Task passes through multiple specialized Agents in sequence; each Agent's output is the next Agent's input. Most common multi-agent pattern — like an assembly line.

Suited for: tasks with clear processing stages requiring different specialized capabilities at each stage. Example: software development pipeline — requirements analysis Agent → architecture design Agent → code implementation Agent → testing Agent → code review Agent.

Pattern 3: Evaluator-Optimizer

A "generator Agent" produces initial results; an "evaluator Agent" reviews quality against preset standards; fails go back to generator Agent for revision; cycle until passing.

Suited for: high-quality output requirements where clear quality standards can be defined. Example: code generation Agent writes code, test execution Agent runs unit tests, test failures return to code generation Agent for revision until all tests pass.

What are the biggest engineering challenges in multi-agent systems? How to reduce risk?

Biggest challenge 1: Error Propagation

In serial multi-agent pipelines, one Agent's wrong output becomes the next Agent's input, processed as correct information — errors can amplify rather than be corrected through propagation.

Risk reduction: add "validation nodes" between key Agents — an independent validation Agent reviews whether the previous Agent's output meets expected format and quality standards; if not, requires redo; only passes to the next Agent if it passes.

Biggest challenge 2: Complexity management

Each additional Agent causes non-linear complexity growth — more message-passing interfaces, more error modes, harder-to-debug problems.

Risk reduction: start with the simplest architecture (use single Agent first, introduce multi-agent only after confirming it can't handle the task well); only add Agents where they genuinely add value. "Don't use six Agents for a problem three Agents can solve."

Biggest challenge 3: Cost accumulation

Multiple Agents running in parallel or series means multiple API calls; costs accumulate quickly. A poorly designed multi-agent system may cost 5-10× a single-Agent equivalent.

Risk reduction: use smaller models (like Haiku) for classification and routing; only use Sonnet or Opus for Agents genuinely needing complex reasoning. Explicitly limit Context size and output length in each Agent's task description.

Claude's actual multi-agent system support — how to use Subagents functionality?

Claude 4 series provides native multi-agent support at the API level, most centrally Subagents (supported in both Claude Code and API).

Using Subagents in Claude Code: a Claude instance (main Agent) can spawn multiple child Claude instances (Subagents) to handle different subtasks in parallel. Example: "Analyze this codebase architecture and generate separate documentation for each major module." Main Agent analyzes codebase, spawns a Subagent for each module, each handles one module's documentation generation in parallel, main Agent integrates all documentation.

Building multi-agent systems in the Claude API: most direct approach is having Claude's Tool Use call a tool that "starts another Claude instance." Each child Agent is an independent API call with its own System Prompt and Context, not sharing memory with the main Agent. Child Agent output returns to main Agent as tool call results.

Anthropic's recommended multi-agent design principles: give each Agent a clear, single responsibility; structure inter-Agent messaging (JSON format) rather than free text; add human confirmation points for high-risk operations; set appropriate Context size limits for each Agent to prevent unbounded Context growth.

Relevance to your work: if your current AI workflow has a task taking over 30 minutes or needing to analyze over 200K tokens of content simultaneously, multi-agent systems may be worth exploring as the next step.