Prompt orchestration

Prompt orchestration is the discipline of coordinating multiple LLM calls — routing, chaining, branching, retrying — to compose a reliable end-to-end workflow from individually less-reliable steps.

Single-prompt apps don't survive contact with diverse real users. Prompt orchestration adds the missing infrastructure: router LLMs to dispatch to the right specialist prompt, chained calls for multi-step tasks, branching on intermediate outputs, retries on failure, structured fallback. The 2026 frameworks (LangGraph, OpenAI Agents SDK, Mastra, Pydantic-AI) treat orchestration as the primary concern. Best practice: keep individual prompts focused, enforce structure between them, and instrument the whole pipeline with traces and evals.

When to use prompt orchestration

• Any production LLM feature with diverse inputs.
• Multi-step workflows where reliability matters.

Common mistakes

• Treating each prompt as independent — orchestration is where the system reliability lives.
• Skipping eval of the orchestration layer — chains hide where quality fails.

FAQ

Related terms

• AI agent — An AI agent is a system where a language model autonomously plans and executes a sequence of tool calls to accomplish a goal.
• Prompt chaining — Prompt chaining splits a complex task into a sequence of smaller LLM calls — each step's output feeds the next — improving reliability over a single mega-prompt.
• Agent loop — An agent loop is the repeating cycle of an AI agent — observe state, decide on an action (usually a tool call), execute, observe the result, and repeat — until a goal is reached or a stop condition fires.
• Model router — A model router picks which language model handles each request based on cost, latency, or task type — the standard production pattern in 2026.

Last updated: 2026-06-01. Raw markdown: https://promtable.com/glossary/prompt-orchestration.md.