Demystifying "Orchestration" - the key to scaling AI
This week we have a guest post from my BCG colleague Juan Martin Maglione. Juan heads up the DEEP GenAI team of 300 engineers, data scientists and product managers who implement AI solutions for customer service. He brings technical expertise alongside a practitioner mindset, having headed up conversational AI at a large global bank prior to his time at BCG.
Recently, I was chatting to Juan about how the word “orchestration” gets used a lot. It’s my nomination for the top buzzword of 2025, and as with a lot of buzzwords, I think it gets used to mean lots of different things.
Juan kindly offered to set out how he sees the role of orchestration in an AI implementation, first explaining that it can actually mean three different things…
Orchestration is everywhere in AI discussions. But when we say “orchestration”, what are we actually talking about?
Is it about workflows? About agents collaborating? About how models decide what to say or which tool to call?
Much of the confusion comes from using one word to describe different coordination problems that sit at different layers of the system. Most AI systems struggle not because models are weak, but because orchestration is blurred. The issue isn’t intelligence. It’s coordination.
A useful way to make this explicit is to separate orchestration into three levels:
Process orchestration
Agent orchestration
LLM orchestration
They are related, but they solve very different problems.
Process orchestration (business logic)
At this level, orchestration is about intentional sequencing of work from a business point of view.
It defines:
which steps exist
which steps are mandatory vs optional
where decisions are taken
where humans must be involved
what happens when something goes wrong
This is not about AI autonomy. It’s about control. Whether implemented via BPM tools, conversational flows, or event-driven systems, workflow orchestration answers a simple question:
what is allowed to happen, and in what order?
A simplified view looks like this:
User request
|
v
[Verify context]
|
v
[Apply business rules]
|
+----> [Needs approval?] ---- yes ----> [Human review]
| |
| no
v
[Execute action]
|
v
[Record outcome / audit]When LLMs are introduced here, they don’t replace the workflow. They sit inside it:
classifying inputs
generating explanations
proposing decisions
But the workflow remains the source of truth for accountability and compliance.
Agents orchestration (technical logic)
Once the business flow exists, the system still needs to run it. This is where orchestration shifts from “what should happen” to “how work gets done”.
Agents orchestration is about coordinating specialized workers:
who does what
how tasks are delegated
how results are combined
how state is preserved across steps
how failures are handled
Here, agents are less like chatbots and more like distributed workers. A typical pattern looks like this:
[Planner agent]
|
--------------------------------
| | |
v v v
[Research agent] [Policy agent] [Execution agent]
| | |
-----------[Shared state]--------
|
v
[Final decision]Key concerns at this layer:
parallel vs sequential execution
retries without duplicating side effects
checkpointing and resuming long-running tasks
timeouts, cost limits, and supervision
human intervention mid-execution
You can have a well-designed business workflow and still fail here if worker orchestration is fragile.
LLM orchestration (linguistic orchestration)
The lowest layer is the one most people encounter first, but it’s often treated too narrowly. LLM orchestration is not just “writing prompts”. It’s about defining the contract between language and action.
This includes:
how instructions are composed
what context is visible to the model
how memory is shaped across turns
which tools exist and how they are described
what output formats are acceptable
Conceptually, it looks like this:
[System instructions]
+
[Relevant context]
+
[Tool definitions]
+
[Output schema]
|
v
[LLM]
|
v
[Text response] or [Structured tool call]At this level, orchestration determines:
whether the model asks for the right tool
whether outputs are usable downstream
whether the model stays within allowed boundaries
whether ambiguity is surfaced or silently guessed
Small changes here can radically affect behavior, even if workflows and agents stay the same.
How the three layers fit together
They stack, but they don’t collapse into one another.
workflow orchestration decides what should happen
worker orchestration decides how work is executed
linguistic orchestration decides how meaning becomes action
A single user request can pass through all three, but each layer answers a different question and should be designed separately.
Conclusion
Most AI systems struggle not because models are weak, but because orchestration is blurred.
When everything is called “orchestration”, teams:
debate prompts when the real issue is missing business rules
build agent frameworks without execution guarantees
optimize language while workflows remain undefined
Treating orchestration as a multi-layer problem makes trade-offs clearer and ownership easier to define.
Some business and strategic questions that follow naturally:
which orchestration layer is a business capability vs a technical one?
who owns workflow logic when AI decisions are involved: business, product, or engineering?
where do you enforce governance: in workflows, in agent execution, or in language constraints?
which layer should be standardized, and which should remain flexible?
how do you test and audit decisions when logic is split across all three?
Curious where others see the biggest friction today: defining the flow, running the workers, or shaping the language.
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 | A guest post by
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Wonderful ...I am finding it most interesting in various complex scenarios to understand the "AI" Human hierarchy as well, where at some condition points, both from a trust and in some cases context perspective, it is important to insert human stage gates. I do feel these checks (I work in Computer systems validation for regulated mfg) are critical even if its just to instill trust as we transition in risk averse industries life life sciences to the more automated orchestration model.