Multi-agent orchestration is the practice of building systems where several AI agents, each specialized in a specific task, work together to handle complex business workflows. Instead of one general-purpose AI trying to do everything, specialized agents divide the work.
A compliance review workflow, for example, might use an intake agent (classifies documents), an extraction agent (pulls key data points), a validation agent (cross-references against records), and a compliance agent (checks regulatory requirements). Each agent is optimized for its specific task, and they pass work between each other through a structured coordination layer.
This architecture mirrors how well-run human teams work: specialists handle their domain, a coordinator ensures handoffs happen correctly, and escalation paths exist for cases that need human judgment.
The main advantage over monolithic AI systems is reliability. When each agent has a narrow, well-defined role, it's easier to test, debug, and improve individual components without affecting the whole system. It also allows for different confidence thresholds — the classification agent might handle 95% of cases autonomously while the compliance agent escalates 30% for human review.
My open-source project Maestro (92 GitHub stars) implements multi-agent orchestration with 30+ specialized agents, and I use the same architecture patterns for client projects.