CyberFreezeDevRouting
Easy requests to a small model, hard ones to a larger model or a person.
Read more about Routing
Send a simple request to a small cheap model and a hard one to a larger model, or to a person.
- Home
- AI glossary
- AI orchestration
What is AI orchestration?
Coordination that turns separate model calls, tools and human approvals into one dependable process.
Definition
Orchestration sets step order, passes data, keeps state, handles retries and approvals, and logs it all. It sits between fixed workflows and free agents.
Read more about Definition
AI orchestration is the coordination of several AI and non-AI components into one dependable process. It decides the order of steps, passes data between them, keeps track of state, chooses between models, handles retries and timeouts, pauses for human approval, and logs everything.
It sits between plain workflow automation, where every path is fixed in advance, and a free-running AI agent, where the model decides its own next step. Most business systems need both: fixed rails for the parts that must always happen, and model decisions inside clearly bounded steps.
What an orchestration layer handles
Tool calls
Models reach APIs and databases through function calling or MCP, with permissions per tool.
Read more about Tool calls
Let models call APIs and databases through function calling or the Model Context Protocol, with permissions per tool.
State and memory
Remembers where a long process stands, so it resumes after a crash or wait.
Read more about State and memory
Remember where a long process is, so it can resume after a crash or a day-long wait for approval.
Error handling
Retries failed calls, checks output against a schema, and falls back when a provider fails.
Read more about Error handling
Retry failed calls, validate model output against a schema, and fall back when a provider is down.
Observability
Logs every prompt, response, tool call, cost and latency so problems can be traced.
Read more about Observability
Log every prompt, response, tool call, cost and latency so problems can be traced.
Tools people use
Popular choices: n8n for visual workflows with AI nodes, LangGraph for graphs of steps with saved state, Temporal for long processes that survive failures.
Read more about Tools people use
Visual platforms such as n8n combine ordinary workflow nodes with AI agent and model nodes. Code frameworks such as LangGraph model a process as a graph of steps with saved state. Durable execution engines such as Temporal keep long-running processes alive across failures. For multi-agent systems, orchestration also covers how agents hand work to each other.
A practical example
-
01 Intake
A supplier's quotation PDF arrives by email; the orchestrator stores it and starts a run.
Read more about Intake
A supplier emails a quotation PDF. The orchestrator stores it and starts a run.
-
02 Extract
A document model pulls line items into a fixed JSON schema, which gets validated.
Read more about Extract
A document model pulls line items into a fixed JSON schema; the orchestrator validates the schema.
-
03 Compare
Last quarter's prices are fetched and an LLM notes the differences.
Read more about Compare
A tool call fetches last quarter's prices; an LLM writes a short note on differences.
-
04 Approve
Orders above a set amount wait for a manager, even if that takes a day.
Read more about Approve
Orders above a set amount wait for a manager in a human in the loop step, which may take a day.
-
05 Act and log
Once approved, a purchase order is created in the ERP and every step is logged.
Read more about Act and log
On approval, a purchase order is created in the ERP and every step's input and output is kept for audit.
Common mistakes
Letting an agent improvise everything
Keep fixed steps fixed; give the model freedom only where judgement is needed.
Read more about Letting an agent improvise everything
Keep fixed steps fixed. Give the model freedom only where judgement is actually needed.
No logs
Without step traces you cannot debug a wrong output or answer an auditor.
Read more about No logs
Without traces of each step you cannot debug a wrong output or show an auditor what happened.
Losing state on failure
Restarting from zero after a crash duplicates emails and orders. Make steps resumable.
Read more about Losing state on failure
A process that restarts from zero after a crash duplicates emails and orders. Make steps resumable and idempotent.
Related terms and pages
Questions
Is AI orchestration the same as an AI agent?
No. An agent is one component that decides its own steps toward a goal. Orchestration is the surrounding structure that runs agents, models, tools and people in a controlled order.
Do I need a framework like LangGraph?
Not always. Many business processes fit well in n8n or a similar platform. Code frameworks help when the logic is complex, needs custom state, or must be tested like normal software.
How does orchestration reduce AI cost?
By routing each request to the cheapest model that can handle it, caching repeated work, and stopping runs early when validation fails instead of passing bad data down the line.
Last updated