Policy for Governing AI Agents in Supply Chain Operations

AI governance in supply chain is less about abstract AI ethics and more about controlling decisions that move inventory, route freight, approve suppliers, and trigger exceptions across a distributed network.

Use a governance-first, bounded-autonomy framework. Give the agent narrow decision rights, require human oversight for high-impact actions, log every decision, and tie the whole system to compliance and data-control rules rather than treating it as a free-running automation tool.

A practical template for a supply-chain AI agent looks like this:

1. Decision charter: define exactly what the agent may do, what it may recommend, and what it may never execute alone. In supply chains, that usually means low-risk tasks like forecasting, drafting supplier shortlists, or flagging exceptions can be automated, while supplier termination, allocation shifts, price commitments, or compliance actions need human approval.
2. Data stewardship: restrict the agent to approved data sources, with lineage, quality checks, and access controls. AI can become noncompliant without a robust company-level data governance strategy, and data poisoning, data drift, and data integrity issues are core governance threats.
3. Policy controls: encode legal, commercial, and ethical rules directly into the workflow; use approval thresholds, role-based permissions, and exception handling.
4. Traceability and auditability: keep immutable logs of inputs, prompts, model outputs, actions taken, and who overrode what. Your supply chain governance framework should emphasize explicit constraints on compliance, auditability, and sovereignty.
5. Monitoring and rollback: monitor performance drift, bias, and unusual behavior; test the agent in simulation before production changes; keep a rollback or safe-stop path. Provision agent drift detection, rollback, and safe recovery, and simulation-based stress testing.
6. Accountability: name a business owner, a technical owner, and an escalation path for incidents.

Minimum go-live standard
Do not deploy an AI agent unless it has a narrow charter, approved data sources, human approval for high-impact actions, immutable logs, and a rollback path. In supply chains, the goal is not just a smarter agent; it is a controllable one that strengthens resilience without creating hidden operational or compliance risk.

References

Karlsen et al., 2026. Responsible AI in Project and Supply Chain Management: A Scoping Review of Governance Mechanisms and Ethical Guidelines (2010–2025). Procedia Computer Science.

Kramer, 2024. Artificial intelligence in the supply chain: Legal issues and compliance challenges. Journal of supply chain management, logistics and procurement.

Shanmugam et al., 2026. Data Poisoning, Data Drift, and Data Integrity in Supply Chain Systems. Sistem Pendukung Keputusan dengan Aplikasi.