Test Whether a Supply Chain AI Agent Is Real or Agent-Washed

Objective

Evaluate whether a vendor’s AI agent has genuine decision and execution capabilities or is mainly a chatbot interface.

Target user

Supply Chain leaders, procurement teams and planning architects.

Inputs

• Vendor demonstration environment
• Sandboxed planning dataset
• Known expected outcomes
• Access and permission documentation
• Audit and rollback evidence

Architecture

Test the agent in a sandbox with no production write access. Observe how it retrieves data, applies constraints, calls tools, preserves state and requests approval.

Steps

• Ask which planning decision the agent improves.
• Verify the data it can access.
• Ask which constraints shaped its recommendation.
• Test whether it calls tools or only generates text.
• Check whether it preserves state across the workflow.
• Identify what it may change without approval.
• Inspect action logs and rollback controls.
• Review how the agent is evaluated.
• Test incomplete and contradictory input data.

Practical test

Use one stable SKU, one promotion, one constrained component, one incorrect lead time and one conflicting planner instruction. Evaluate whether the agent detects the data issue, separates facts from assumptions, identifies the constraint, asks for approval before a high-impact action and produces an auditable recommendation.

Validation

• The agent explains the decision and active constraints.
• Tool calls are visible and authorized.
• High-impact actions require approval.
• Logs preserve data, prompt, policy and model versions.
• Rollback succeeds.

Governance box

• Data source: sandboxed planning dataset.
• Owner: planning-process owner.
• Validation: known expected answer and failure test.
• Version control: prompt, policy and model version.
• Access: no production write access during evaluation.
• Manual override: required.
• Failure mode: stop execution and preserve logs.