The Situation
Developers increasingly relied on AI for coding help, but most tools pulled them out of their IDE and into separate chat or documentation experiences, increasing context switching and reducing efficiency. Debugging, optimization, and compliance often required fragmented workflows, slowing task completion and lowering trust in generated code. The problem was how to embed AI assistance directly into the IDE to help developers complete tasks faster, more reliably, and with greater cost efficiency—without breaking focus.
The Strategy
I focused on inserting AI assistance directly into IDE workflows, framing the agent as an in-context pair programmer rather than a standalone tool. The experience was designed to support debugging, optimization, and implementation at the moment of need, allowing developers to stay in flow while working in their existing environment.
To increase relevance and trust, I emphasized agent messaging and knowledge cross-pollination across code context, documentation, and inferred intent. This enabled the agent to deliver targeted guidance—ranging from error handling to optimizations and informational prompts—without requiring developers to manually search or reframe their problem.
I took a phased approach, starting with foundational capabilities such as code completion, tutorials, and bug troubleshooting, while establishing the scaffolding for more advanced integrations over time. This allowed the experience to scale in sophistication without overwhelming users or disrupting existing workflows.
The Results
- Built a working prototype demonstrating in-IDE AI assistance across debugging, optimization, and documentation use cases
- Validated feasibility of delivering educational and reference content directly in context
- Explored opportunities to improve code quality, development velocity, and prompt efficiency
- Helped shape early product vision for in-IDE, task-oriented coding agent experiences
Amara Hulslander – UX Designer 