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
- Connects AI to real business systems with permissions
- Turns a model into a process that produces an outcome
- Underpins teammates that own a job end to end
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 — it reads, checks against your documents, uses your connected tools, and drafts an outcome, all coordinated for you. You don't wire up the steps like an automation; you describe the job and the teammate orchestrates the work, then hands you a draft to approve.
Related terms
Agentic AI is artificial intelligence that plans and takes multi-step, autonomous action toward a goal — rather than just generating a single piece of text in response to a prompt. Where a standard generative model answers what you ask, agentic AI decides what needs to happen, breaks the goal into steps, uses tools to carry them out, and adapts as it goes, working with limited human input.
AI workforceAn AI workforce is a coordinated set of AI teammates that each own a recurring business function — handling real work end to end rather than answering one-off prompts. Unlike a single chatbot, a workforce is multiple specialised teammates that share context and hand work between them.
AI agentAn AI agent is software that perceives its environment, decides what to do, and takes action toward a goal — often autonomously and over multiple steps — by using tools, calling APIs, and reasoning with a language model. Unlike a chatbot that only generates text in reply to a prompt, an agent can act on the world: it can search, read, write to systems, and chain steps together to complete a task rather than just answer a question.