Internal Procurement Portal

Inventory monitoring and reorder recommendations by default — optional, human-enabled submission with safety checks.

Build an agent that checks inventory levels in your Fabric workspace via a data agent, identifies items below their reorder threshold, and produces a prioritized reorder report: what to reorder, how much, from which supplier, and what to escalate. By default it recommends only — nothing is submitted. When you explicitly enable autonomous mode, it navigates your procurement portal via browser automation to submit the approved reorders, applying safety checks at every step: standard reorders within approved limits proceed, while value overruns, duplicate orders, inactive suppliers, and zero-stock items are flagged for manual review. An optional API cross-check can verify inventory against a live source.

This is a monitor → recommend → (optionally) act with safety gates pattern using numbered-rule prompt structure. The redesign leads with the safe, high-value job — monitoring and recommendations — and treats real-world action as an advanced, opt-in capability. Because the agent can submit real purchase orders, the instructions build in multiple safety layers (max order values, duplicate detection, supplier validation, a portal-outage fallback) and require a human to review the report; the numbered rules make every decision auditable against a specific rule.

This pattern applies anywhere an agent monitors a system, recommends actions, and can optionally execute approved ones within human-controllable boundaries. Common scenarios for this pattern:

• Supply replenishment for retail or warehouse operations
• Service-ticket escalation and routing on threshold rules
• Infrastructure scaling requests from monitoring data
• Recurring purchase-order generation from consumption data
• Maintenance work-order creation from equipment readings

This manifest uses: data agent, browser automation, OpenAPI

How to build multi-turn agents using numbered rules prompt structure for reorder report output.

This agent manifest is sample code subject to Azure's licensing terms for Distributable Code under the Universal License Terms for Online Services .

IMPORTANT! This manifest is for educational and experimentation purposes. Resulting agents are not production ready. Review all provided resources and carefully test agent behavior in the context of your use case. Agents you create may be subject to legal and regulatory requirements, may require licenses, or may not be suitable for all industries, scenarios, or use cases. By using any manifest, you are acknowledging that resulting agents and other output are solely your responsibility, and that you will comply with all applicable laws, regulations, and relevant safety standards, terms of service, and codes of conduct. Learn more .