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Inventory Manager

<p>The Inventory Manager Agent is built to assist users in managing inventory, forecasting stock levels, ordering out-of-stock products, and maintaining accurate order statuses through smart, data-backed decisions and relevant tool usage.</p><p>When a user issues a request, the agent begins by understanding the intent, whether it involves checking current inventory, forecasting sales, calculating stockouts, placing orders, or updating deliveries. It then dynamically selects the appropriate tools required to fulfill the request, avoiding unnecessary tool calls to optimize performance and clarity. The tools are executed in the correct sequence, respecting their input-output dependencies (e.g., forecasts must precede comparison, and comparison must precede ordering). The agent then compiles the results from the tools, presents only factual information without making assumptions, and communicates a clean summary back to the user.&nbsp;</p><p>At all times, the agent relies solely on tool outputs to drive its responses.</p><p></p><p>-----</p><p><strong><em>Disclaimer:</em></strong><em> AI agents are powered by LLMs, which generate responses based on patterns in data rather than fixed rules. As a result, their behavior is </em><strong><em>non-deterministic</em></strong><em>, meaning outputs may vary even for similar inputs. This may lead to situations where the agent </em><strong><em>fails to trigger expected tool calls</em></strong><em> or behaves in ways that differ from prior interactions. Please account for this variability when designing or using AI agents</em>.</p><p><br></p>

The Inventory Manager Agent is built to assist users in managing inventory, forecasting stock levels, ordering out-of-stock products, and maintaining accurate order statuses through smart, data-backed decisions and relevant tool usage.

When a user issues a request, the agent begins by understanding the intent, whether it involves checking current inventory, forecasting sales, calculating stockouts, placing orders, or updating deliveries. It then dynamically selects the appropriate tools required to fulfill the request, avoiding unnecessary tool calls to optimize performance and clarity. The tools are executed in the correct sequence, respecting their input-output dependencies (e.g., forecasts must precede comparison, and comparison must precede ordering). The agent then compiles the results from the tools, presents only factual information without making assumptions, and communicates a clean summary back to the user. 

At all times, the agent relies solely on tool outputs to drive its responses.

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Disclaimer: AI agents are powered by LLMs, which generate responses based on patterns in data rather than fixed rules. As a result, their behavior is non-deterministic, meaning outputs may vary even for similar inputs. This may lead to situations where the agent fails to trigger expected tool calls or behaves in ways that differ from prior interactions. Please account for this variability when designing or using AI agents.


库存管理代理旨在协助用户进行库存管理、预测库存水平、订购缺货产品,并通过智能、数据支持的决策和相关工具的使用,保持订单状态的准确性。


当用户发出请求时,代理会首先理解其意图,可能包括:

  • 检查当前库存

  • 预测销售情况

  • 计算缺货情况

  • 下达订单

  • 跟踪订单交付状态并将新库存更新至系统

随后,代理会动态选择合适的工具来完成请求,避免不必要的工具调用,以优化性能和提升清晰度。工具的执行顺序严格遵循其输入输出依赖关系(例如,预测必须在比较之前进行,比较必须在下单之前进行)。代理将工具的结果汇总后,仅呈现事实信息,不做任何假设,并向用户传达简洁明了的总结。


注意:代理始终仅依赖工具的输出来驱动其响应。

智能仓库管理系统


使用 Agent workflow 作为工具,能有效灵活的掌握实际库存

View tool call history andagent response
View Agent conversation
OpenAI API Key
Credentials Configuration
Authenticate to AI provider
OpenAI Authenticator
Select Model: gpt-4.1Max response length: at least 1000-2000
OpenAI LLM Selector
Lists all the tools available to the agent
List Files/Folders
Chat with the agent
Agent Chat View
Agent task
Agent Prompter
Connection of workflowto tools
Workflow to Tool
Debug: Forecast_Sales

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Extensions

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