What is agent orchestration? Frameworks, runtimes, and observability explained

Agent orchestration in AI involves the coordination of agent behavior across three distinct layers: expression, runtime, and observability. The ongoing debate between single-agent and multi-agent systems fundamentally hinges on how these layers are approached, as exemplified by contrasting views from Cognition and Anthropic. Cognition argues against multi-agent systems due to context-sharing issues, while Anthropic demonstrates their effectiveness in parallel tasks, revealing that both perspectives are valid within different task contexts. The expression layer focuses on the control flow and framework of agents, with most disputes centered here. The runtime layer, akin to Kubernetes in web development, is maturing with solutions like Temporal and Google's AX, offering durability and reliability in long-running tasks. Observability, however, is where significant challenges remain, as understanding and debugging agent failures requires sophisticated trace-level insights, which are essential for improving reliability. The key takeaway is that effective agent orchestration requires careful consideration of all three layers, with context engineering being crucial for both single and multi-agent systems.

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