LLM Orchestration
LLM orchestration refers to the systematic coordination and management of multiple language model calls within AI systems and agent architectures. Rather than relying on a single model invocation to solve a problem, orchestrated systems decompose tasks into sequences of model interactions, routing information between different models, agents, or specialized components based on task requirements. This approach enables more complex reasoning and task completion by breaking down problems into manageable steps and leveraging the strengths of different models or agents at different stages.
Core Functions
Orchestration systems handle several key responsibilities: routing queries to appropriate models or agents, managing context and state across multiple interactions, sequencing operations in logical order, and aggregating results from parallel or sequential executions. These systems may coordinate between general-purpose language models, specialized models trained for specific domains, external tools, retrieval systems, and decision-making agents. The orchestrator determines when to invoke each component, what information to pass along, and how to combine outputs into coherent results.
Practical Implementation
In practice, LLM orchestration appears in applications like multi-step reasoning systems where an initial model decomposes a problem, subsequent models address sub-problems, and a final stage synthesizes answers. Agentic systems use orchestration to manage tool use, where language models decide which tools to invoke, process the results, and determine next steps. Retrieval-augmented generation systems orchestrate between search components and language models. These patterns allow systems to handle complexity, improve accuracy on specialized tasks, and maintain better control over model behavior compared to single-model approaches.