What is a Multi-Agent System?

Multi-agent systems split a complex goal into sub-goals and assign each to a specialized agent. Rather than prompting one general-purpose LLM to do everything, you build a small team — a planner, a researcher, a writer, a verifier — and let them pass artifacts between each other. The pattern mirrors how human companies organize: specialization beats generalization once a task crosses a certain complexity threshold. Technically, a multi-agent system has three load-bearing components. First, agents: each is typically an LLM with a custom system prompt, a scoped tool set, and its own memory. Second, a communication layer: agents pass structured messages (JSON, text, or function-call shapes) that another agent can read and act on. Third, a coordinator: sometimes called a supervisor, orchestrator, or router — it decides which agent runs next, passes context between them, and handles failures. Anthropic's 2024 research on 'orchestrator-worker' patterns and OpenAI's Swarm / Assistants API both formalize these roles. Why does this work better than a single mega-prompt? Context windows. Even with Claude 4.5's 200K or Gemini's 1M+ tokens, a single agent loaded with every tool, document, and instruction for a large task starts producing degraded outputs (needle-in-a-haystack failure, instruction drift). Splitting responsibilities into 5-10 smaller agents — each with a focused prompt and narrower tool set — produces more reliable results, parallelizes naturally, and makes failures easier to debug because you can see exactly which agent failed. Typical patterns include: (1) hierarchical, where a manager agent delegates to workers; (2) peer-to-peer, where agents of equal status negotiate; (3) pipeline, where agents are chained like Unix pipes; (4) market-based, where agents bid on tasks. Most production systems today use the hierarchical pattern because it matches how humans organize work and because debugging a tree is easier than debugging a graph. Tycoon is itself a multi-agent system: the founder talks to Astra (the AI CEO), who dispatches work to specialized agents — an AI CMO for marketing, AI CTO for engineering, AI COO for operations. Each agent has its own memory of its domain, its own tools, and its own autonomy level. The founder never has to manage the interaction between agents — Astra does that. This is the same pattern used by AutoGen (Microsoft), CrewAI (open-source), and LangGraph (LangChain's graph-based orchestration framework), but wrapped in a business-operator interface rather than a developer framework.