AI automation
Use models inside controlled workflows where classification, extraction or drafting benefits from judgement.
AI handles a bounded step while rules, approvals and system updates remain inspectable.
People repeat interpretation work, but the acceptable answer and escalation boundary are unclear.
The task has examples of acceptable and unacceptable outputs.
A bounded AI workflow with evaluation cases, controls and exception routing.
Low-confidence or consequential outputs go to a person.
- Define the task
People repeat interpretation work, but the acceptable answer and escalation boundary are unclear.
- Prepare approved context
Trigger → prepare context → model task → validate → approve or route
- Evaluate the response
Low-confidence or consequential outputs go to a person.
- Escalate when needed
A plausible output passes validation but contains the wrong customer or source context.
- Record the outcome
AI handles a bounded step while rules, approvals and system updates remain inspectable.
The straightforward path is only half the design.
The owner still needs to be visible when input is ambiguous, a provider only partly succeeds or a person needs to take over.
Open controls and recovery detail
Human controls
- 01Low-confidence or consequential outputs go to a person.
- 02The operating owner for ai automation must be able to approve, pause or reverse its consequential step.
Failure modes
- 01A plausible output passes validation but contains the wrong customer or source context.
- 02AI automation is not retried until the earlier provider outcome has been checked.
Work you can inspect
See the system, what was exercised and the status of the evidence. Open the technical record for source, date and limits.
Workflow control prototypes
Reference prototypes have exercised event identity, partial-failure recovery, follow-up stops and deterministic reporting against documented synthetic scenarios.
Inspect technical record
- Source
- Keystone prototype 01–06 acceptance records
- Artifact
- Acceptance suites and post-build audits
- Boundary
- These are reference implementations, not customer case studies; several provider paths remained simulated.
- Freshness
- review annual · review by 2027-08-12
Sources behind the explanation
Current platform documentation and implementation principles sit here, separate from Keystone delivery evidence.
Controlled workflow design principles
Consequential workflows need named ownership, stable event identity, visible exception states and a defined human authority for ambiguous or irreversible actions.
Open source and scope
- Source
- Keystone engineering policy derived from implementation and acceptance-test practice
- Boundary
- These principles guide design. They do not prove a particular workflow has been deployed or will produce a commercial result.
- Freshness
- stable
What affects the scope?
What does AI automation involve?
A bounded AI workflow with evaluation cases, controls and exception routing. Scope is agreed after the current process, access and acceptance cases have been inspected.
What could prevent AI automation from being the right next step?
The task cannot be evaluated or errors would create unacceptable harm.
What proof supports this AI automation page?
The evidence below is limited to the recorded Keystone test or demonstration relevant to AI automation; its label explains what was and was not exercised.
Describe where the work around AI automation gets stuck and what should happen next.
The request keeps this page and its intent attached. A person reviews the context before any customer-facing follow-up.
Discuss this workflow