API (Application Programming Interface)
A defined way for one system to request data or actions from another. Good API design for AI returns clean, consumer-relevant fields with clear names, is versioned so changes do not silently break consumers, and includes metadata such as freshness and source.
Defined in 3 GAGE programs, which carry 3 distinct definitions of it. The wording above is taught in Certified AI Transformation Professional (CATP).
How each discipline defines it
The same term does different work depending on who is using it. These are the definitions as each program teaches them, unedited.
A defined way for one system to request data or actions from another. Good API design for AI returns clean, consumer-relevant fields with clear names, is versioned so changes do not silently break consumers, and includes metadata such as freshness and source.
A defined way for one software system to request data or actions from another. A realistic test environment for an agent typically includes the real or staged APIs the agent calls as part of its normal workflow.
A connection method that lets different software systems share data automatically; some free tools (Zapier, Make) provide API connections without coding.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- Data Readiness for Small Operations · Digital Foundations, Certified AI Practitioner: Workplace Foundations
- Build the Test Environment Before You Trust a Single Score · Testing and Red-Teaming, Certified Agentic AI Governance Professional (CAAGP)
- Enterprise Integration Strategy · Technology, Platforms and Vendors, Certified AI Transformation Professional (CATP)
Where it shows up at work
A definition is where API (Application Programming Interface) starts. The lessons above teach it inside the competencies below, which are what job descriptions ask for and what the GAGE credential records. Every link here is drawn from the same careers map that builds a role's readiness check.
Designs tests for factuality, robustness, fairness, safety and abuse resistance with rubrics, baselines and thresholds, and says what a score misses.
One lesson on this page teach it as a primary outcome.
Decides where AI should be used, where it should not, and which use cases create measurable value, tied to the organization's priorities.
One lesson on this page teach it as a primary outcome.
Builds minimization, purpose limitation, de-identification, consent and retention into an AI system before launch, with tests that prove it.
One lesson on this page teach it as a primary outcome.
Roles that hire for it
Each role page shows the full competency set, what the seat decides, and a readiness check against your own evidence.
- Chief Technology Officer, AI engineering editionneeds it at depth
- Model Risk Managerneeds it at depth
- AI Evaluation Specialistneeds it at depth · 6 open postings this week
- AI Model Validatorneeds it at depth
- AI Product Manager, responsible product editionneeds it at depth · 3 open postings this week
- Chief AI Officerneeds it at depth
How job descriptions say it
Postings rarely use the textbook word. These are the phrases the careers map reads as asking for the competencies above.
Where the frameworks place it: NIST AI RMF MEASURE 1 and 2; NIST AI 600-1; ISO/IEC 27701; GDPR Article 25.
One interview question about it
Design the evaluation for a customer-service model before launch. What do you test, against what data, and what result blocks the release?
Asked of candidates for ai evaluation and testing design. Answering it in your own words is exactly how a GAGE topic is passed, and the credential records the pass where an employer can check it.
If you come from another field
Some backgrounds already carry this competency, and the readiness check gives credit for it.
- Model risk, credit risk and quantitative analysis carries it as work you can show: Test design and benchmark selection are the craft.
- A computer science or software engineering degree carries it as a claim to back up: Testing is second nature; AI evaluation design is a new shape of it.
- A data science, statistics or analytics degree carries it as a claim to back up: Evaluation design for a governance decision is a new frame for a known skill.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.