Integrating business systems with AI agents

The problem: a system built only for people

Requirements and work items lived in a separate requirements management system, built for people to use through a web interface.

Left as it was, people would have to copy AI's output back in by hand, and progress reporting would become yet another manual job.

The approach: wrap the API in a CLI and connect the agent

We wrapped the requirements management system's API in a CLI (command-line tool) and integrated it with the AI agent. People use the web UI; the AI agent uses the CLI; both work on the same data.

Because the AI agent doing development worked in the terminal, a thin CLI over the API became the official integration, and we didn't build a separate MCP server. The CLI lives in the same repository as the API, so when the API changes, the CLI changes in the same commit.

The result: the AI agent became a participant that keeps records

Once integrated, the AI agent's job changed.

• It reads requirements directly and uses them as development context.
• It writes PRDs and attaches them to the requirement.
• It breaks work into items and registers them.
• It records the start and end of each step itself.

Progress comes from records

Progress is no longer a number someone typed into a spreadsheet; it's calculated from the records of steps that actually finished. The AI agent became not just a tool that produces output but a participant that leaves a record of the work.

Lesson: integration matters more than the form

API, CLI or MCP — choose the integration that fits your situation. What matters is not the form but whether your business systems are integrated with AI agents at all.