Agentic AI
AI that takes multi-step autonomous actions toward a goal, where the action is the output, actions chain, and speed can remove the human reaction window, requiring oversight controls enforced outside the agent's reach; the trustworthy controls are hard boundaries enforced below the AI, a plan-then-act gate (a human approves the whole plan before any step executes), and a kill switch the agent cannot rewrite.
Defined in 3 GAGE programs, which carry 11 distinct definitions of it. The wording above is taught in Certified AI Practitioner: Workplace Foundations.
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.
AI that takes multi-step autonomous actions toward a goal, where the action is the output, actions chain, and speed can remove the human reaction window, requiring oversight controls enforced outside the agent's reach; the trustworthy controls are hard boundaries enforced below the AI, a plan-then-act gate (a human approves the whole plan before any step executes), and a kill switch the agent cannot rewrite.
An AI system that takes multi-step actions on a user's behalf, such as calling other software or executing transactions, with limited human review at each individual step. The newest and fastest-moving of the three AI categories, and the one most likely to fall into definitional gaps in older statutes.
AI systems that take actions in the world through tools, not just produce outputs for a human to act on. The defining property is agency: the system can choose and execute actions, so its mistakes are actions that already happened rather than recommendations you can decline.
AI that does tasks rather than only answering questions. It takes a goal, breaks it into steps, and carries them out, checking in rather than waiting to be told every move. Its arrival shifts the human skill from asking to directing and supervising.
AI tools that take a sequence of actions autonomously across multiple steps rather than answering a single prompt; they automate parts of the lower literacy layers but raise the stakes of a gap, because an early error propagates silently through later steps. The literacy skill they demand is checkpoint thinking: knowing where a human must verify and being able to audit what the agent did.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- Why You, Why Now · Your AI Learning Companion, Certified AI Practitioner: Workplace Foundations
- Interconnected Literacies: How Digital, Data, and AI Literacy Connect · Digital Foundations, Certified AI Practitioner: Workplace Foundations
- What is AI? Definitions, History, and Types · AI Fundamentals, Certified AI Practitioner: Workplace Foundations
- AI as Thinking Partner vs. Working Partner · AI Fundamentals, Certified AI Practitioner: Workplace Foundations
- Orchestration Protocols for Human Oversight · Critical Thinking and Context Engineering, Certified AI Practitioner: Workplace Foundations
- Working Inside an Agent Policy · Agentic AI and Workforce Integration, Certified AI Practitioner: Workplace Foundations
- The Only Ten Things About AI That Matter in Policy · The Point Person Mandate, The AI Lobbyist: Certified AI Policy Strategist
- The Liability Risk Assessment and Mitigation Strategy · Legal Frameworks and Liability Navigation, The AI Lobbyist: Certified AI Policy Strategist
- Set Up Your AI Transformation Workspace · Your AI Transformation Command Center, Certified AI Transformation Professional (CATP)
- Frontier AI, Autonomous Agents and Emerging Paradigms · Risk, Resilience and Frontier AI, Certified AI Transformation Professional (CATP)
Where it shows up at work
A definition is where Agentic AI 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.
Uses generative AI tools daily, knows what a model can and cannot do, and can say where an output should not be trusted.
2 lessons on this page teach it as a primary outcome.
Governs AI agents that take actions: tool access, least privilege, interruptibility, cascading actions and accountability for what an agent did.
One lesson on this page teach it as a primary outcome.
Defines who reviews AI outputs, what they check, when they can override, and how to keep review from becoming a rubber stamp.
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.
- Operations Manager, AI-readyneeds it at depth
- AI Adoption and Enablement Leadneeds it at depth
- AI Literacy Leadneeds it at depth
- AI Controls Analystneeds working knowledge
- AI Governance Coordinatorneeds working knowledge
- Chief Information Security Officer, AI security focusneeds 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: EU AI Act Article 4; DOL AI Literacy Framework; IMDA Model AI Governance Framework for Agentic AI; NIST AI RMF.
One interview question about it
Walk me through a task you now do with an AI tool. Where did you stop trusting its output, and how did you know?
Asked of candidates for working ai fluency. 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.
- Recent graduate in law, policy, business or data carries it as a claim to back up: Most graduates already use the tools daily.
- Teachers, trainers and instructional designers carries it as a claim to back up: Most educators are already using AI in preparation.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.