Multi-Agent Systems: When You Need More Than One AI

Key strengths: built-in persistence (Postgres/Redis state storage), human-in-the-loop checkpoints, and LangGraph Cloud for production. Best for complex workflows that need audit trails and resume capability.

AutoGen (Microsoft)

AutoGen focuses on conversational agents: agents communicate via message threads and respond to each other. The classic pattern is a coder agent + executor agent — one writes code, one runs it and reports errors, looping until it passes.

from autogen import AssistantAgent, UserProxyAgent

coder = AssistantAgent("coder", llm_config={"model": "gpt-4"})
executor = UserProxyAgent(
    "executor",
    code_execution_config={"work_dir": "workspace"}
)

executor.initiate_chat(coder, message="Build a web scraper for product prices")

Best fit: coding tasks, research workflows, anything where agents need to self-debug.

CrewAI

CrewAI offers higher abstraction: define a Crew of Agents with specific roles (Research Analyst, Content Strategist, Editor) and separate Tasks. Fast to prototype, but less flexible for complex production edge cases.

Framework Comparison

Architecture Patterns

Orchestrator-Worker: A coordinator agent decomposes the task, delegates to specialized workers, and aggregates results. The orchestrator manages flow — workers do the actual work.

Orchestrator
  ├── Research Worker  (web search, data collection)
  ├── Analysis Worker  (data processing, statistics)
  └── Writer Worker    (content generation, formatting)

Sequential Pipeline: Output from Agent A feeds into Agent B. Use when data dependencies are strict: cleaning → analysis → visualization → report.

Parallel Fan-out/Fan-in: Orchestrator spawns N workers simultaneously, waits for all to complete, merges results. Use for competitor analysis (scrape 10 sites at once), batch processing, or content A/B generation.

Real Cost Analysis

Multi-agent doesn't just double your costs. Each agent needs its own context, system prompt, and typically makes more LLM calls. Real token costs often run 3–5x higher than an optimized single agent.

Add to that: orchestration logic is code — it needs testing, maintenance, and its own monitoring as an independent service.

Decision Checklist

Answer these 5 questions before committing to multi-agent:

1. Can the task decompose into independent subtasks? If not, single agent handles it better.
2. Which subtasks actually run in parallel? If all sequential, multi-agent doesn't help latency.
3. Is your current context window large enough? If yes, no need to split.
4. Does your team have capacity to maintain the added complexity? Orchestration logic needs owners.
5. Can your budget absorb 3–5x token costs? If not, optimize single agent first.

Multi-agent is the right tool for the right problem. But most real-world problems are still solved better by a well-designed single agent. See AI Agent memory and context management to optimize single agents, and agentic workflow architecture to build a solid foundation before scaling up.