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 28 distinct definitions of it. The wording above is taught in Agentic AI Governance: Applied Mastery.
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 7 topics of every program are free with a free account.
- AI Output Ownership and Accountability Frameworks · Ethical and Responsible AI and Operational Governance, AI Literacy & Professional Conduct
- EU AI Act Article 4 & US AI Workforce Mandates: Compliance Requirements · Ethical and Responsible AI and Operational Governance, AI Literacy & Professional Conduct
- Why Agentic Breaks Generative Governance · Taking the Controls, Agentic AI Governance: Applied Mastery
- The Whole Framework, Worked · The Agent, Deconstructed, Agentic AI Governance: Applied Mastery
- Dossier Workshop A: System Description · The Agent, Deconstructed, Agentic AI Governance: Applied Mastery
- The Agentic Value Chain · Meaningful Human Accountability, Agentic AI Governance: Applied Mastery
- Who Pays When the Agent Errs I · The Law and the Regulators, Agentic AI Governance: Applied Mastery
- The vendor's black box: forcing provenance answers from a supplier who has none · Poison, Leaks, and the Adversary, AI Data Governance: The Data Chair
- Article 10 executed: the EU AI Act's data governance duty for the system your organization ships · Lineage Under Audit, AI Data Governance: The Data Chair
- Fixing the model, breaking it again: why fixes are never free · Build Before You Govern, AI Governance: Applied Mastery
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.