Decision rights that are explicit and machine-readable.
Decision authority can be distributed across approval matrices, delegation policies, operating practice, and undocumented knowledge. Agents need those boundaries made explicit.
Overview
Decision authority can live in approval matrices, delegation policies, operating practice, and undocumented knowledge. Agents need explicit, encoded decision rights: what an agent may decide, which conditions require escalation, who owns the boundary, and how an action can be reviewed.
Encoding decision rights is not only a technical exercise. It surfaces ambiguities in ownership, approval thresholds, policy exceptions, and review responsibilities that need an accountable business decision.
Why it matters
A machine-readable policy provides a boundary that can be inspected. The encoding work makes unwritten escalations, inconsistent thresholds, and nominal approvals visible for review before they are applied by an agent.
The resulting framework supports bounded agent action and gives auditors, regulators, boards, and operators a common record of the policy being applied.
Implementation requirements
Decision rights sit between the data foundation and the workflow foundation. Without trustworthy data, encoded decisions act on the wrong inputs. Without encoded decisions, redesigned workflows cannot run without a human gate at every step, which defeats the purpose of the redesign.
In the five-level autonomy model, the move from Level 2 to Level 3 is the move from humans approving each task to humans setting policy and handling exceptions. The policy must exist in a form the agent can read for that move to be real. Until then, what looks like autonomy is automation with longer queues.
Capability focus
Aiger Data connects policy discovery, threshold reconciliation, exception-handling rules, and a machine-readable policy framework to the orchestration layer. The work makes ambiguities visible so decision boundaries can be reviewed before agents act inside them.