Building Multi-Agent Systems: When One AI Isn't Enough

The single-agent architecture works until it doesn't. For contained tasks — summarize this document, answer this question, write this code — one agent with the right tools and a good prompt is usually the right answer. The problems appear when tasks require parallelism, when they exceed what fits in a single context window, or when they need multiple specialized capabilities that conflict with each other in a single system prompt.

The clearest example of a context window problem is large document processing. An agent tasked with analyzing a 300-page legal agreement can't fit the entire document in its context. You can chunk and summarize, but information that spans chunks gets lost. The better architecture is a supervisor agent that breaks the document into sections, dispatches worker agents to analyze each section in parallel, and aggregates their outputs. Each worker gets full fidelity on its section; the supervisor synthesizes the structured results. This runs in roughly 1/N of the time and produces better output than sequential chunking.

Specialization is the second driver of multi-agent design. A single agent system prompt that tries to be a legal analyst, a financial modeler, and a customer service agent is worse at all three than three specialized agents. Specialization in prompting is analogous to specialization in software architecture — a function that does one thing does it better than a function that tries to do everything. Routing queries to the right specialized agent based on intent classification is the core of what frameworks like LangGraph formalize.

Orchestration patterns fall into a small number of repeating shapes. The supervisor-worker pattern is the most common: a coordinator agent decomposes a task, assigns subtasks to worker agents, and synthesizes results. The pipeline pattern passes outputs from one agent to the next in sequence, where each agent transforms or enriches the output of the previous one. The parallel dispatch pattern runs multiple agents on the same input simultaneously and aggregates their outputs — useful for multi-perspective analysis or when you need multiple tools checked in parallel.

Agent handoffs are the source of most production failures in multi-agent systems. When the supervisor passes context to a worker agent, that context must be sufficient for the worker to do its job without needing to query back for clarification. Poorly designed handoff schemas result in workers making assumptions, going off-track, or returning outputs that the supervisor doesn't know how to integrate. We've found that defining the handoff schema explicitly — a typed struct with all required fields — is more reliable than passing natural language instructions between agents.

State management across a multi-agent system requires more deliberate design than single-agent state. Shared state that multiple agents read and write needs locking or optimistic concurrency control. Long-running workflows that span multiple agent calls need a durable state store so the workflow can be resumed after a failure without starting over. For multi-step workflows that might take minutes to complete, we use Redis for fast shared state and Postgres for durable workflow checkpoints.

The failure modes in multi-agent systems are multiplicative rather than additive. If one agent has a 95% success rate, a three-agent pipeline where all three must succeed has a 0.95³ = 86% end-to-end success rate. This compounds quickly as pipelines get longer. Error handling in multi-agent architectures needs to be more aggressive than in single-agent systems: early detection, explicit fallback paths, and clear signals from worker agents about their confidence in their output.

LangGraph is currently the most useful framework for orchestrating complex multi-agent workflows in production. It gives you explicit state management, conditional branching, human-in-the-loop integration points, and streaming output from any node in the graph. The learning curve is real but the abstractions are worth it for systems beyond toy complexity. For simpler pipelines, building directly on top of the provider SDK with custom routing logic is often cleaner and easier to debug.