Build an Enterprise Quote and Approval Assistant
Build an agent that converts a sales request into a calculated quote with product lines, discounts, dates, and approval requirements. Let reviewers adjust the quote and inspect every pricing decision.
What you are building
The core problem, expected build, and operating context for this challenge.
A deal desk preparing custom product quotes.
Build an agent that converts a sales request into a calculated quote with product lines, discounts, dates, and approval requirements. Let reviewers adjust the quote and inspect every pricing decision.
Sales request
Product price list
Proration and approval policies
Extract the requested products, quantities, and contract dates.
Calculate line totals using code and supplied pricing rules.
Identify discount or margin exceptions.
Generate a versioned quote and approval request.
A quote editor with line calculations, policy explanations, approval status, and an exportable quote.
Produce a quote with list price 3600, discounted price 2880, and a pending approval for the 20 percent discount.
A working local application with the specified review interface.
Source code, dependency lockfile, and setup commands.
A recorded end-to-end demo using the supplied starter data.
Example outputs, action logs, and automated tests.
These fictional records are a small development fixture, not a production dataset. Implement the mock services described in the brief.
```json { "request": { "product": "Support Suite", "seats": 10, "months": 12, "requested_discount_percent": 20 }, "price_list": [ { "product": "Support Suite", "monthly_seat_price": 30 } ], "policy": { "maximum_unapproved_discount_percent": 10 }, "approval": null } ```
Use fictional pricing rules. Do not issue binding quotes or infer real tax treatment. Choose an available model that supports your implementation. Record its identifier and version.
Review the complete application, not only the starter-case answers.
The quote shows list and discounted totals.
The discount exception requires approval.
Changing the quote invalidates approval of an older version. Add one normal case, one incomplete-input case, and one failure-recovery case.
Participation guide
Use fictional pricing rules. Do not issue binding quotes or infer real tax treatment.
How work is evaluated
Review workflow completion, interface usability, evidence quality, and safe failure handling. No agent execution has been verified.
A working local application with the specified review interface. Source code, dependency lockfile, and setup commands. A recorded end-to-end demo using the supplied starter data. Example outputs, action logs, and automated tests.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Extract the requested products, quantities, and contract dates.
Calculate line totals using code and supplied pricing rules.
Identify discount or margin exceptions.
Generate a versioned quote and approval request.
Reference links and supporting material
Supplied fictional starter records: Sales request, Product price list, Proration and approval policies. The overview includes the complete JSON bundle. Implement local mock services and extend the sample coverage.
Challenge setup
Configuration summary for this challenge.
Challenge input
Input details are not configured.
Pydantic AI
Typed Python agent framework.
Evaluated output
No output checks are configured.
- No evaluation checks are configured.
- 1 public reference case
- 1 configured tool
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
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Configured tools
- Pydantic AI ยท Optional
Evaluation contract
- No evaluation checks are configured.
Reference cases
- 1 public reference case
Workflow tasks
No workflow tasks are configured.
Recipe freeze marker
No recipe freeze time is recorded.