Building a Multi-Agent System from Scratch: Architecture Patterns

Multi-agent systems divide complex work among specialized agents, but this adds coordination challenges that a single-agent demo does not reveal. Three architectures suit different kinds of tasks. A pipeline passes information through a fixed sequence of agents, making each stage easy to test when the workflow is predictable. An orchestrator-worker system has a central agent assign input-dependent subtasks and combine results; it supports flexible decomposition but can become a bottleneck and spread planning errors. A blackboard lets agents independently contribute hypotheses, evidence, or critique to shared state, which works when the next step cannot be known in advance, but makes behavior harder to reconstruct and evaluate. The choice depends on whether the workflow is fixed, its steps are known but task-specific, or its path emerges through discovery.

Production systems also face delegation loops, excessive model calls and costs, conflicting concurrent updates, and errors that cascade when downstream agents trust a hallucinated claim. These are system-level problems, so testing agents in isolation or asking them to behave better is not enough. Enforce delegation-depth and task-wide token or cost limits in code, and provide a human-escalation route when confidence is low or a limit is reached. Splitting work can make prompts narrower and evaluation more manageable, but it does not inherently improve correctness: multiple agents can compound one another’s mistakes. Reliability depends on evaluation and guardrails as much as on the architecture.