What is LLM orchestration?

LLM orchestration is the code that coordinates language model calls into a working system: it holds state between steps, decides which model handles which step, calls tools, retries and handles failures, and records what happened. The model itself is one component inside it, since a model turns one input into one output and cannot sequence its own work. Major starts from the opposite assumption to most designs: the agent reasons once and writes a deterministic app, so settled steps stop calling a model.

What is the difference between an LLM and an orchestrator?

An LLM turns one input into one output, holds no memory between calls, and cannot act outside its own response. An orchestrator is the code around it: it holds state, chooses which model handles which step, calls tools, handles failures, and records what happened. Anything the orchestrator can do in code does not need a model call. Major pushes that line as far as it goes, keeping the model for judgment and the app for everything settled.

What is an example of an LLM orchestrator?

LangGraph, CrewAI, AutoGen, LlamaIndex, and Haystack are the frameworks most teams reach for. A typical orchestrated workflow: a document arrives, a model classifies it, a parser extracts fields, code validates totals and matches a purchase order, the result writes to the system of record, and only genuine ambiguity goes back to the model. On Major, an agent builds that pipeline as a deployed app with its own database.

Do I need an orchestration framework?

Not if your workflow is a single prompt with one or two tools, because a framework adds a dependency you will maintain. Reach for orchestration when you have branching control flow, state that outlives a single call, and failure paths that need explicit policy. Even then, deciding which steps need a model matters more than the framework. Major fits once a workflow has stabilized enough to be written down as code.

How does LLM orchestration reduce cost?

Two levers do the work. Routing sends cheap steps to smaller models, prompt caching charges cache hits at a tenth of base input price, and the Batch API halves asynchronous work; those lower the slope of a bill that still rises with every run. The second lever moves a stabilized step into deterministic code, so it costs the same at a million runs as at one. Major front-loads cost into the reasoning that produces the app, then flattens it.

Is LLM orchestration the same as AI orchestration?

LLM orchestration is the narrower term, covering control flow, routing, state, and tool calls around language models specifically. AI orchestration is the wider coordination of AI systems across an organization, including non-language models, data pipelines, and human approval steps. Both describe coordination that runs through a model on every pass, which Major changes by turning the repeatable middle of a pipeline into software the agent deploys and then runs.

LLM Orchestration: The Layer That Should Use Fewer Model Calls

Most orchestration guides teach you to chain more model calls together. That is the expensive path. Here's what LLM orchestration actually is, the patterns worth knowing, and why the best orchestration layer keeps shrinking the work the model does.

Rahul RamakrishnanAugust 26, 2026Updated October 21, 2026

Conductor directing an orchestra with a baton, a metaphor for LLM orchestration

LLM orchestration is the code that coordinates language model calls into a working system. It holds state between steps, routes each step to a model or to plain code, calls tools, handles failures, and records what happened. The model is one component inside it. An LLM turns one input into one output with no memory; the orchestrator is everything around that call.

The industry definition stops at coordinating model calls, and that guarantees cost scales with usage. A better orchestration layer keeps shrinking the model's share of the work. On Major, the enterprise platform where agents build the software they run on, an agent reasons once about a repeatable step, builds a deterministic app for it, and runs the app from then on. The agent layer decides. The app layer executes and remembers.

What does an orchestration layer actually do?

IBM calls the orchestration layer "the backbone of the LLM app stack". Accurate, and incomplete. It does two jobs with different economics.

Deciding is judgment on inputs nobody enumerated: what kind of document this is, whether a discrepancy is rounding or a dispute. That needs a model.

Executing has one correct answer that does not change between runs: parse the fields, run the arithmetic, join against the purchase order, write the row, emit the log line. That does not need a model once someone has worked out how.

A poorly designed layer runs both jobs through the model and calls the result a chain. A well-designed one keeps them visibly separate and keeps moving work from the first job into the second.

Three terms get mixed up. Orchestration decides what runs, in what order, with what state. An agent framework such as LangGraph or CrewAI is a library for writing that orchestration, so adopting one is a dependency decision, not an architecture. A gateway or router picks which model serves a call and handles keys, fallback and spend limits. It feeds orchestration but does not replace it.

Why do model costs grow with every run?

A prompt chain that costs cents in a notebook costs real money in production, because the cost is per run instead of per capability.

Take an inbound document workflow with five model calls per document. At 10,000 documents a month you pay for 50,000 calls. At 100,000 documents you pay for 500,000. The line is straight and stays straight.

Anthropic's published pricing charges prompt cache hits at 0.1x the base input rate and gives the Batch API a 50% discount on asynchronous work. Both are worth doing. Both lower the slope of a line that still rises with every run. A step that has moved into code has no slope, because running it a millionth time costs what the first run cost.

Tool definitions add a recurring charge too. Every tool schema you attach is paid for on every call, used or not. Attaching twelve tools to a step that only ever calls two is a standing tax.

Retries compound it. A retried model call costs full price, and a chain that retries at three points can multiply its worst-case spend on exactly the runs already going badly. Decide up front which failures retry, which fall back to a cheaper deterministic path, and which stop and escalate to a person.

Which steps need a model at all?

Label every step in the document workflow honestly.

Classify document type: judgment at first, repeatable once the type set closes. Model, then a classifier.
Extract fields from a known vendor layout: repeatable. Deterministic parser.
Extract fields from an unseen layout: judgment. Model.
Validate totals and required fields: repeatable. Code.
Match to a purchase order: repeatable. A database query.
Write to the system of record: repeatable, and a side effect that must be logged. Code with a scoped credential.
Resolve an invoice-to-contract mismatch: judgment with money attached. Model, escalating to a person.

Five of seven steps do not need a model once the workflow has run for a while. They needed one to be figured out. That is a different claim, and it is the whole argument.

Move a step only when it has stabilized. Log real inputs and outputs across a production window, not a curated test set. Ask whether identical inputs must produce identical outputs. Measure what share of observed inputs fall into shapes you can enumerate. Shadow the code version against the model on live traffic, and promote it when the disagreement rate stops moving. Keep the model as the fallback for inputs the code rejects. Skipping that last step produces a system that is cheap, fast and wrong on the slice of documents that miss the template.

Build the orchestration layer on Major

Here is how the document workflow runs on Major.

Connectors: Gmail receives vendor documents, NetSuite or Postgres holds purchase orders, and Slack carries escalations. Each is a Major connector with scoped credentials.
The app that holds state: an intake app with a managed database for documents, extracted fields, match results and exception status, plus file storage for the originals. State lives here, not in a context window.
The deterministic app logic: validation, the purchase order join and the write to the system of record are code the agent built once. They run the same way every time.
The agent and its judgment: it classifies unseen layouts, reads contract language, and decides whether a mismatch is rounding or a dispute worth a person's time.
The trigger: a workflow fires when a message lands in the intake mailbox, or on a schedule for batch runs.
Governance: role-based access on the app, credentials scoped to what each step needs, and audit logs showing which step ran, under whose authority, and what it wrote.

As the library of apps grows, later workflows lean on apps that already exist and need less reasoning. You can start building this free.

What do people get wrong about orchestration?

Picking a framework is not the architecture decision. LangGraph and CrewAI will both faithfully execute an expensive design. The architecture decision is which steps need a model.

Orchestration also does not mean multi-agent. Multi-agent is one topology. Most production workloads are one reasoning path with tools attached, and agents that converse with each other add model calls faster than they add capability.

Caching and cheaper models change the slope, and only moving work out of the model flattens the curve.

Deterministic is not always safer either. Code is predictable within the input distribution it was written for and brittle outside it. A step that has not stabilized belongs in the model, and saying so is part of the discipline.

What we believe, and what this does not cover

We think the orchestration layer's job is to make itself smaller over time. That is a position, and teams running exploratory work whose shape changes every run will find less to move. If a workflow fits in one prompt with a tool or two, you do not need any of this.

The model does not go away on Major. It still handles unseen inputs, contract ambiguity and the call on when to escalate. It stops re-deriving the arithmetic. This piece also does not address evaluation, the harder and separate problem of knowing whether a model step's output is good.

Start building on Major

Describe the repeatable steps in your busiest multi-system workflow and let an agent turn them into governed apps with their own database, logs and permissions. Judgment stays with the model; everything settled runs as code.

Frequently asked questions

What is LLM orchestration?: LLM orchestration is the code that coordinates language model calls into a working system: it holds state between steps, decides which model handles which step, calls tools, retries and handles failures, and records what happened. The model itself is one component inside it, since a model turns one input into one output and cannot sequence its own work. Major starts from the opposite assumption to most designs: the agent reasons once and writes a deterministic app, so settled steps stop calling a model.
What is the difference between an LLM and an orchestrator?: An LLM turns one input into one output, holds no memory between calls, and cannot act outside its own response. An orchestrator is the code around it: it holds state, chooses which model handles which step, calls tools, handles failures, and records what happened. Anything the orchestrator can do in code does not need a model call. Major pushes that line as far as it goes, keeping the model for judgment and the app for everything settled.
What is an example of an LLM orchestrator?: LangGraph, CrewAI, AutoGen, LlamaIndex, and Haystack are the frameworks most teams reach for. A typical orchestrated workflow: a document arrives, a model classifies it, a parser extracts fields, code validates totals and matches a purchase order, the result writes to the system of record, and only genuine ambiguity goes back to the model. On Major, an agent builds that pipeline as a deployed app with its own database.
Do I need an orchestration framework?: Not if your workflow is a single prompt with one or two tools, because a framework adds a dependency you will maintain. Reach for orchestration when you have branching control flow, state that outlives a single call, and failure paths that need explicit policy. Even then, deciding which steps need a model matters more than the framework. Major fits once a workflow has stabilized enough to be written down as code.
How does LLM orchestration reduce cost?: Two levers do the work. Routing sends cheap steps to smaller models, prompt caching charges cache hits at a tenth of base input price, and the Batch API halves asynchronous work; those lower the slope of a bill that still rises with every run. The second lever moves a stabilized step into deterministic code, so it costs the same at a million runs as at one. Major front-loads cost into the reasoning that produces the app, then flattens it.
Is LLM orchestration the same as AI orchestration?: LLM orchestration is the narrower term, covering control flow, routing, state, and tool calls around language models specifically. AI orchestration is the wider coordination of AI systems across an organization, including non-language models, data pipelines, and human approval steps. Both describe coordination that runs through a model on every pass, which Major changes by turning the repeatable middle of a pipeline into software the agent deploys and then runs.