Can Your AI Complete the Task? Look Beyond the Chat Window
A customer asks whether you can supply parts at agreed pricing by a needed date. Answering takes several connected steps. Here is how to tell a chat window apart from an agentic workflow.
A customer asks: "Can you supply these parts for our application, at our agreed pricing, by the date we need them?"
A useful response requires several steps: interpret the requirements, research the products, check account pricing, retrieve inventory information, and identify anything that needs confirmation.
This is where the difference between a basic LLM wrapper and an agentic system becomes visible.
A basic wrapper puts a chat interface around a language model, sometimes with access to documents. An agentic system can coordinate multiple tool calls, use each result to determine the next step, and carry out an authorized workflow.
Consider these three examples.
Product research that leads toward a quote
A basic chat implementation might summarize a specification sheet or suggest a product based on uploaded documents.
An agentic workflow can interpret the customer's requirements, search technical sources, compare candidate products, and ask for missing operating details. It can then use ERP tools to retrieve account-specific pricing and availability and assemble the information needed for a quote.
Each result affects what happens next. An unavailable product may require researching another option. An unverified compatibility requirement should trigger clarification or engineering review.
Purchase orders that move into the ERP workflow
A basic chat implementation might extract line items from a PDF and present them as text.
An agentic workflow can extract those items, look up the customer, resolve product references, check quantities and pricing, flag discrepancies, and prepare an order for review.
That requires several connected operations, with checks between them. A mismatched price needs attention before submission; a missing product reference needs resolution.
Widge processes PDF and Excel purchase orders into supported ERP workflows, with human confirmation before order submission.
Collections follow-up informed by account history
A basic chat implementation might draft a payment reminder from information an employee provides.
An agentic workflow can retrieve outstanding invoices, review relevant customer history, identify a previously reported dispute, and prepare the appropriate follow-up under the company's collections procedure.
The next step may be a reminder, a request for internal review, or help resolving a billing issue. Widge's tested AR collections workflow illustrates how agents can coordinate business information and follow-up tasks.
Ask to see the decisions between tool calls
The distinction is not simply how many tools a system can call. Some chat applications support tools, and a fixed sequence of API calls is not enough to demonstrate adaptable execution.
Ask what happens when a lookup fails, information conflicts, or approval is required. A capable agentic system should handle those conditions within defined boundaries and leave a traceable record of its actions.
Bring Widge a request that takes your team several steps to complete. We'll demonstrate the supported workflow, the tools involved, and where your people remain in control.
See how Widge answers your team's questions
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