Workflow Orchestration
Workflow orchestration refers to the automated coordination and sequencing of multi-step processes in which AI agents, tools, APIs, and systems collaborate to achieve a higher-level goal. Unlike simple automation that executes linear scripts, an orchestration layer manages step ordering, error handling, retries, parallel execution, and state flow between components. In AI systems, workflow orchestration typically covers agent coordination (multiple specialized agents receive subtasks and pass results downstream), tool call management (controlling which tools fire when and how outputs feed into subsequent steps), state management (persisting context and intermediate results across steps), and error handling (automatic retries, fallback paths, and escalation on unexpected states). Popular frameworks include n8n, Temporal, Apache Airflow, and vendor-specific solutions such as Anthropic Managed Agents or LangGraph. The choice of orchestration framework significantly determines a system's scalability, maintainability, and cost profile. For production-grade AI systems, professional orchestration is not an optional add-on but a prerequisite for reliable, maintainable, and scalable agent workflows.