Decision Assurance for Agentic AI: Detecting Drift Before It Compounds

Autonomy changes the unit of risk

A conventional model produces an estimate for a person or system to use. An AI agent can observe, decide, act and create the next state of the workflow. In procurement it may select suppliers, request quotations and recommend an award. In logistics it may reroute work and alter capacity. In workforce planning it may move tasks between teams.

A small error in one step may be harmless. Repeated decisions can make the error directional. The agent changes the cases it sees, the data recorded and the constraints faced by later decisions. In this article, agentic drift means a sustained departure of the workflow's decisions or outcomes from the conditions under which the system was approved. It is an operating risk, not a formal new statistical category.

Monitor the residual, the action and the outcome

Monitoring only model accuracy is too narrow. Decision assurance follows the input distribution, the model's residual error, the actions selected, override rates and the resulting business outcome. The expected residual can be standardised against a validated baseline.

z_t=\frac{e_t-\mu_e}{\sigma_e}

A control rule can flag a large departure, while an exponentially weighted moving average can detect a smaller sustained shift.

s_t=\lambda z_t+(1-\lambda)s_{t-1}

These statistics are evidence for investigation. They do not explain the cause on their own. A change may come from demand, policy, data quality, a vendor update or the agent's own behaviour.

Set limits around the workflow

Marketways defines control at three levels. Model limits cover calibration, stability and performance for important groups. Decision limits define which actions are permitted, how rapidly they may accumulate and where a human must intervene. Business limits track loss, service, conduct, safety or another outcome that management ultimately owns.

The combination matters. A model can remain statistically accurate while an optimisation objective drives undesirable behaviour. A workflow can also meet its average target while producing concentrated errors in a sensitive product or customer group. AI and Model Risk addresses the model; Decision Assurance examines the complete decision and the authority around it.

Decision sovereignty needs evidence

Decision sovereignty means the organisation retains the ability to understand, constrain, challenge and reverse the actions delegated to AI. It requires an inventory of agentic systems, named owners, versioned policies, traceable decisions and tested fallback routes. It also requires thresholds that relate to business consequence rather than generic technical alerts.

This is especially relevant to regulated UAE operations. The Central Bank of the UAE states that senior management and boards remain accountable for AI and ML systems and should receive regular performance and risk reporting. It also expects model validation, sensitivity analysis, stress testing and ongoing monitoring. Delegating execution does not delegate accountability.

A practical assurance cycle

Before deployment, Marketways defines the decision, baseline, permissible actions and failure consequences. During a controlled release, we compare the agent with the existing route and inspect overrides and exceptions. After deployment, monitoring distinguishes random variation from a sustained change and triggers investigation, constraint or suspension at an agreed boundary.

Agentic AI Design and Deployment builds these controls into the workflow. Risk Detection identifies changing signals. Model Evaluation and Validation tests model performance. The wider series explains how probabilistic decision rules and causal evaluation strengthen the same operating system.