Deployer
The value-chain role occupied by the organization that installs, configures, and operates an agent platform for its own purposes. IMDA's nine OpenClaw recommendations are addressed to this role; governance responsibility for how the platform is configured and used does not transfer upstream to the platform provider simply because the platform provider wrote the underlying code.
Defined in 7 GAGE programs, which carry 29 distinct definitions of it. The wording above is taught in Certified Agentic AI Governance Professional (CAAGP).
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.
The role in the framework's value chain occupied by the organisation putting an agent into operation, choosing what systems it connects to, what permissions it carries beyond the platform's defaults, and what instructions it is given. The deployer's choices sit closest to the moment of harm and are the most common role held by the learner working through this program's dossier.
Under Article 3(4), any natural or legal person, public authority, agency, or other body using an AI system under its authority, except where the AI system is used in the course of a personal non-professional activity. Deployers of high-risk systems bear the Article 26 operational obligations and, where Article 27(1) applies, the FRIA obligation.
The actor that uses an AI system under its own authority in its activity. Most organizations are deployers; deployers of high-risk systems carry Article 26 duties (and some an Article 27 fundamental-rights impact assessment), and must know each system's tier to meet those duties and to catch an under-classifying vendor.
Under the EU AI Act, an organization that uses a high risk AI system under its own authority without being the system's provider; carries a narrower but real set of obligations, including human oversight and, following the Digital Omnibus, potential access to the Article 4a bias detection basis.
An entity that puts an already-built AI system to use in a specific context, such as an employer using a third-party hiring tool, as distinct from the developer that built the underlying model. Many AI laws impose obligations specifically on deployers, regardless of who built the model.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- AI Output Ownership and Accountability Frameworks · Ethical and Responsible AI and Operational Governance, Certified AI Practitioner: Workplace Foundations
- EU AI Act Article 4 & US AI Workforce Mandates: Compliance Requirements · Ethical and Responsible AI and Operational Governance, Certified AI Practitioner: Workplace Foundations
- Why Agentic Breaks Generative Governance · Taking the Controls, Certified Agentic AI Governance Professional (CAAGP)
- The Whole Framework, Worked · The Agent, Deconstructed, Certified Agentic AI Governance Professional (CAAGP)
- Dossier Workshop A: System Description · The Agent, Deconstructed, Certified Agentic AI Governance Professional (CAAGP)
- The Agentic Value Chain · Meaningful Human Accountability, Certified Agentic AI Governance Professional (CAAGP)
- Who Pays When the Agent Errs I · The Law and the Regulators, Certified Agentic AI Governance Professional (CAAGP)
- The vendor's black box: forcing provenance answers from a supplier who has none · Poison, Leaks, and the Adversary, Certified AI Data Governance Professional (CADGP)
- Article 10 executed: the EU AI Act's data governance duty for the system your organization ships · Lineage Under Audit, Certified AI Data Governance Professional (CADGP)
- Fixing the model, breaking it again: why fixes are never free · Build Before You Govern, Certified AI Governance Professional (CAIGP)
Where it shows up at work
A definition is where Deployer 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.
Traces where data came from, what transformed it, who owns each hop and where it flows downstream, so a number can be defended.
2 lessons on this page teach it as a primary outcome.
Classifies a system by role and risk tier, knows which obligations bind on which date after the Digital Omnibus, and what evidence conformity needs.
One lesson on this page teach it as a primary outcome.
Turns controls into enforceable obligations: data use, change notice, audit rights, incident duties, subcontractors, IP, exit and deletion.
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 Data Officer, data leadership editionneeds it at depth
- AI Regulatory Counselneeds it at depth · 14 open postings this week
- AI Privacy Engineerneeds it at depth · 6 open postings this week
- Data Governance Leadneeds it at depth · 2 open postings this week
- Privacy Analystneeds working knowledge
- Third-Party AI Risk Analystneeds 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: EU AI Act Article 10; NIST AI RMF MAP 4; Regulation (EU) 2024/1689; Regulation (EU) 2026/1744.
One interview question about it
Trace the lineage of a training dataset back to its source. What do you record, and what breaks when you cannot?
Asked of candidates for data lineage and provenance. 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.
- Privacy and data protection carries it as work you can show: Data maps and records of processing are lineage by another name.
- Data analysis, stewardship and business intelligence carries it as work you can show: Source-to-report lineage is what you trace.
- A data science, statistics or analytics degree carries it as a claim to back up: Lineage and provenance are known ideas, rarely yet documented for an auditor.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.