AI orchestration

AI orchestration is coordinating multiple AI components — models, tools, data sources, and steps — so they work together to complete a multi-step job. Instead of one prompt to one model, orchestration sequences the work, passes context between steps, and connects to the right systems.

Key characteristics

• Coordinates multiple steps, tools, and data sources
• Passes context between steps so work builds up instead of restarting
• Connects AI to real business systems, within the access it has been given
• Turns a model into a process that produces an outcome
• Underpins AI teammates that own a job end to end

What an orchestration layer actually does

A language model on its own takes text in and returns text out. Almost every useful business job needs more than that, and the orchestration layer is what supplies it:

• Planning. It breaks a goal ("check this loan package and chase what's missing") into steps, and works out which steps depend on which. Steps that don't depend on each other can run side by side; steps that do wait for their inputs.
• Tool calls. It decides when to read a file, search the web, look up a record in a connected tool, or draft a message — and passes each result into the next step.
• Context management. It carries forward what matters (the checklist, the client's name, what's already been found) without flooding every step with everything.
• Hand-offs. In multi-agent systems, it routes part of the job to a more specialised agent and brings the result back.
• Limits. It caps the number of turns and stops loops, so a confused run ends instead of spinning.

Orchestration vs automation

Workflow automation runs fixed steps you design and maintain: when X happens, do Y. It is excellent for deterministic plumbing — moving a form submission into a spreadsheet, say. Orchestration coordinates AI that can read, judge and adapt between steps, which is what you need when the input varies: a document package that's missing a different item every time, or an email that could be a complaint, a question or an order. Most teams keep both. See AI agents vs automation vs workflows for where each fits.

Example

Handling a forwarded document package means orchestration: read the email, open each attachment, check them against a grounded checklist, look up the client record, and draft a reply — several steps and tools coordinated into one outcome.

How it relates to Kuvai

Orchestration is what happens under the hood when a Kuvai teammate does its job. You don't wire up the steps; you describe the job, and the teammate plans the work as a set of dependent steps run in parallel waves, using Kuvai's built-in tools and the tools you've connected and assigned to it. Any teammate can hand part of a task to another teammate (up to two levels deep), and the delegation notes appear in chat. Every reply shows its steps in a collapsed "Transparency" trail, so you can see what was read, checked and drafted. You decide how far it goes: ask for drafts and you get drafts; ask it to act and it acts. Work it starts on its own never makes destructive or out-of-lane changes without checking with you first.

Frequently asked questions

What is AI orchestration?

AI orchestration is coordinating multiple AI steps, tools and data sources to complete a multi-step job, passing context along the way. A Kuvai teammate orchestrates this for you — reading, checking against your documents, using your connected tools, and drafting an outcome — without you building a workflow.

How is orchestration different from automation?

Automation runs fixed rules you build and maintain; orchestration coordinates AI that can read, judge, and adapt across steps. Automation suits predictable plumbing; orchestration suits work where every case is a little different.

Do I need to build the orchestration myself?

Not with a teammate. Frameworks let engineering teams build their own orchestration; a Kuvai teammate already plans and coordinates its steps, so you describe the job instead of wiring it.