Feature flag
A configuration mechanism that enables new code to be merged to main but not activated in the production environment until a runtime flag is toggled. Used to integrate long-running feature branches incrementally, avoiding the drift caused by long-lived branches.
Defined in 3 GAGE programs, which carry 3 distinct definitions of it. The wording above is taught in Foundations of Robotics and Physical AI.
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 configuration mechanism that enables new code to be merged to main but not activated in the production environment until a runtime flag is toggled. Used to integrate long-running feature branches incrementally, avoiding the drift caused by long-lived branches.
A switch, decoupled from the code deploy, that turns a feature on or off in seconds without shipping new software. Per-segment flags provide the granularity that lets you disable a harmful segment while keeping the rest.
A switch that turns an AI feature on or off instantly without a full redeployment, enabling near-instant rollback of a problematic feature.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- Shipping the feature: rollout, monitoring, and the rollback you hope not to use · Shipping AI and Surviving the Incident, Certified AI Governance Professional (CAIGP)
- AI System Reliability and Safety Engineering · Risk, Resilience and Frontier AI, Certified AI Transformation Professional (CATP)
- Git branching, pull requests, and code review on a team · Software Engineering and Team Workflow, Foundations of Robotics and Physical AI
Where it shows up at work
A definition is where Feature flag 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.
Plans for a vendor failure, an unsafe model change or a suspended service: rollback, manual fallback, data export and safe decommissioning.
2 lessons on this page teach it as a primary outcome.
Classifies AI incidents by severity, runs containment, preserves evidence, manages notification, and closes the loop with lessons learned.
2 lessons on this page teach it as a primary outcome.
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.
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.
- AI Incident Response Leadneeds it at depth · 1 open posting this week
- Chief Information Security Officer, AI security focusneeds it at depth
- Chief Risk Officer, AI risk editionneeds working knowledge
- AI Product Manager, responsible product editionneeds it at depth · 3 open postings this week
- AI Security Architectneeds working knowledge · 8 open postings this week
- AI Vendor Risk Managerneeds working knowledge
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: DORA; NIS2; NIST AI RMF MANAGE 4; EU AI Act Article 73.
One interview question about it
Your AI vendor shuts down next month. What was in your continuity and exit plan, and what did you fail to plan for?
Asked of candidates for ai resilience, continuity and exit planning. 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.
- Cybersecurity and IT carries it as a claim to back up: Continuity and recovery planning are part of the job.
- A cybersecurity or information assurance degree carries it as a claim to back up: Incident response is a course; AI incidents add model failure to the playbook.
- Model risk, credit risk and quantitative analysis carries it as work you can show: Test design and benchmark selection are the craft.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.