Prompts and inputs
Shape the request and create the structure needed by later steps.
AI orchestration guide
The model is only one part of the job
A useful AI workflow usually needs information from somewhere, rules about what may happen and a clear point where a person takes responsibility. Orchestration connects those parts and decides which service should handle each step.
Building blocks
What has to happen around the model
Knowledge retrieval
Find approved organisational context before a model prepares a result.
Model deployments
Route each task to a configured local or external model suited to its role.
Branches and conditions
Run independent work in parallel or choose a path based on process data.
Human approval
Pause before a consequential step so a person can review and decide.
System connections
Use approved service, search or data connections to obtain information or continue an action.
Related terms
Similar words, different amounts of freedom
| Term | Common emphasis | Useful evaluation question |
|---|---|---|
| AI workflow automation | A defined sequence of AI and non-AI steps | Can the process be versioned, reviewed and operated reliably? |
| AI orchestration | Coordination across models, knowledge, tools and decisions | Are routing, data paths and dependencies visible? |
| AI agent orchestration | Coordination of components that select or perform tasks | How much autonomy is allowed and where are its limits? |
| Human-in-the-loop AI | A person reviews, corrects or approves part of the process | Is human action meaningful, timely and recorded? |
Workflow design
Decide who is responsible before choosing what to automate
Define the outcome
State what the workflow should produce and who remains responsible for using it.
Map the information path
Identify every source, model and external service that can receive or return data.
Choose the control points
Add validation, conditions and human approval where the process can become unsuitable or consequential.
Plan failure and change
Define what happens when a dependency fails and how a new workflow version reaches operation.
Measure the intended quality
Test the complete workflow against representative tasks and explicit acceptance criteria.
AI orchestration questions
Why use more than one AI model?
Different models can suit different tasks, environments, costs or data paths. Having a choice is useful, but someone still needs to test the options and own the way they are run.
Does orchestration make AI autonomous?
Not necessarily. An orchestration workflow can be tightly defined, include fixed branches and pause for people. The permitted autonomy is a design choice and should be explicit.
What does human in the loop mean?
It means a person participates at a defined point, such as reviewing, correcting, approving or rejecting an output before the process continues.
How does Pūnaha approach orchestration?
Pūnaha provides visual, versioned workflows with triggers, retrieval, models, data validation, decisions, documents, connections, durable messages, waits, human work, evidence checkpoints and reusable subprocesses.
How do BPMN processes and adaptive cases fit?
Use BPMN-based process modelling for repeatable operational paths and CMMN-based adaptive cases when evidence and judgement determine the next appropriate work. Both can connect to Pūnaha workflow modules and governed decisions.
Follow one workflow from the first input to the final review
Take the flexible AI workflow tour, or ask us to walk through a process from your own organisation.