I've been evaluating Cursor's agentic capabilities against more traditional IDE + Copilot workflows for internal tooling, and decided to put it through a real-world test: building a CLI tool to reconcile our ERP's inventory snapshots with real-time data from our 3PL's warehouse management system API. The goal was a single command that could flag discrepancies above a configurable threshold for manual review, outputting a formatted report. I allocated a single afternoon for the entire development lifecycle.
My workflow, which I documented meticulously, proceeded as follows:
* **Phase 1: Specification & Scaffolding (30 minutes).** I began by creating a new project directory and a detailed `SPEC.md` file within Cursor. This file outlined the tool's purpose, required command-line arguments (`--erp-file`, `--3pl-api-key`, `--threshold-percent`), the expected JSON output structure, and the specific API endpoints from our 3PL's documentation. I then asked Cursor's Agent to "create a Python CLI tool based on this specification." It generated the `pyproject.toml` with `click` and `requests` dependencies, the main CLI skeleton, and a basic project structure.
* **Phase 2: Core Logic Implementation (75 minutes).** This was the most intensive part. Using Cursor's Chat with the specific `SPEC.md` and our internal API documentation open in the context, I worked module-by-module.
* I instructed it to "implement the ERP file parser for CSV format A," and it produced a function with proper error handling for missing columns.
* For the 3PL API client, I pasted a sample curl command from the docs and asked to "create a client class using this authentication method and the /inventory endpoint." It built the class with headers and a retry mechanism.
* The reconciliation engine was built via a conversation: "Now create a function `reconcile` that takes the erp_items list and 3pl_items list. It should match on SKU, calculate variance as a percentage of ERP qty, and return a list of discrepancies where absolute variance exceeds the threshold." The agent provided a clear implementation, which I then refined with a follow-up: "Add a flag to the discrepancy dict for `erp_higher` boolean."
* **Phase 3: Output Formatting, Polish, and Debugging (45 minutes).** With core functions written, I tasked the agent with "creating the report generator" for both console (colored, tabular) and JSON file output. It used `rich` for console formatting after I approved its suggestion. The final debugging involved real data. I used a sanitized sample CSV and a mocked API response (via a local JSON file) to run the tool. Cursor's ability to run the script and show errors in-line was critical here; a `datetime` serialization issue in the JSON output was identified and fixed by asking the agent to "make the discrepancy list JSON-serializable."
The attached screenshots show the key stages: the initial specification document, the agent generating the first module, the iterative building of the reconciliation logic, and the final terminal output showing a successfully run report with colored highlights for variances.
This exercise demonstrated a significant reduction in boilerplate creation and context-switching. The agent's maintained awareness of the project specification (`SPEC.md`) across the entire session prevented the typical drift seen when asking isolated Copilot prompts. The throughput for a well-scoped, single-purpose B2B integration tool was objectively higher. However, the workflow requires disciplined, modular prompting—treating the agent as a junior developer you are directing with precise, incremental tasks—rather than expecting a single magical command to produce the final product. The tool is now in use by our logistics team, and the entire codebase remains under 300 lines of clean, maintainable Python.
Data over opinions