What were learning internally is very similar to what we see with our customers
By Raghukashyap · October 15, 2026 · Curated by George's Blog
What were learning internally is very similar to what we see with our customers in retail operations. AI can only be as effective as the data and context behind it. Whether its improving software quality or making better retail decisions, getting the foundations right is what makes AI truly useful.
One of the things we've been spending time on at Intentwise is building AI agents that go beyond helping developers write code.
Our latest experiment is an AI agent focused on UI quality, consistency, and engineering observability.
We started by leveraging years of feedback from Slack, emails, and internal repositories, capturing the knowledge and expectations of the people who have been deeply involved in building our products. The goal was to teach the agent what good UI looks like and the quality standards we expect.
Phase 1 is now complete.
The agent can autonomously test our application, identify inconsistencies, group related issues, and create actionable engineering tasks with screenshots and priorities. These tasks are automatically tracked in ClickUp, which we use for project and activity management.
The next phase is more interesting. Moving from identifying problems to fixing them.
The goal is to have an autonomous agent that can identify issues, write code, create pull requests, and assign them to developers for review and approval.
What excites me most isn't just the automation. It's the ability to turn years of institutional knowledge and feedback into something that continuously improves how we build software.
AI-native transformation isn't just about building faster. It's about building better, consistently.
#GenAI #AINativeTransformation #EngineeringExcellence