We've been running an internal experiment at Pro-Sharp with agentic AI tools and it's reshaped how our developers approach their work.
The shift has been clear. Our team spends less time on the mechanics of writing code and more time on the questions that actually matter.
Here's what we've learned about making AI work as a development tool:
We've built custom integrations that let our developers tap into contextual information when investigating legacy code the kind with no documentation, where the original developer is long gone and the logic is anyone's guess. Instead of spending days manually tracing dependencies, the system surfaces relevant context and helps map what's actually happening. Hours instead of days.
Bug tracing time dropped from 3 hours to 1. The old loop of googling error messages and skimming StackOverflow is gone. Code quality improved because we're catching edge cases that manual review would miss.
The AI functions as a capable junior assistant for execution work. Unit tests, boilerplate, and repetitive-but-necessary tasks are handled by senior developers, allowing them to stay focused on architecture and problem-solving. The role division is straightforward: we design and validate, the AI executes and generates.The practical results speak for themselves.
Our team's take? This workflow works. We're integrating it into how we build for clients.
Curious what this could look like for your team? Let's talk.