Recent advances in LLMs have transformed AI coding assistants from simple autocompletion tools into conversational partners that support a wide range of development tasks through natural language interaction. While this shift promotes closer human-AI collaboration, this also places a burden on developers to craft precise prompts and integrate sufficient context to accomplish their task.
To address this, there is growing interest in proactive AI systems that can anticipate developer intent and surface timely, contextually relevant suggestions - potentially revealing insights developers might not have considered.

However, existing research on proactive coding assistance has largely been confined to controlled, experimental settings, leaving open questions about its effectiveness and integration in real-world development environments.

In this work, we design and implement a proactive AI assistant within JetBrains Fleet, a polyglot enterprise IDE. Our system signals AI activity via in-editor cues and delivers context-aware code improvement suggestions via a chat interface. We adopt heuristics based on IDE activity to determine when it is timely to intervene, during various stages across the development workflow: formulating needs, idea execution and committing changes.
Lastly, we conducted a five-day in-the-wild study with 18 professional developers to evaluate its impact on user experience and engagement patterns.

Our findings demonstrate that timing is a critical aspect influencing perceived value of proactive AI assistance.
Suggestions delivered at natural workflow boundaries (e.g. post-commit) achieved the highest engagement, while mid-task interventions were often dismissed.
The consistent engagement trends (and voluntary continued use) indicate strong potential for integrating proactive AI into everyday software engineering workflows.
Our study not only underscores the feasibility and practical value of proactive assistance in enterprise development environments, but also provides actionable design insights for future tools.
While challenges remain - such as improving suggestion relevance and supporting diverse user preferences - these highlight important directions for advancing adaptive, intent-aware proactive systems that can further enhance developer workflows.

The development of contemporary source code auto-completion tools have significantly boosted productivity and efficiency of developers. In 2021, the GPT-2-based Transformer CodeGPT was developed to support code completion and text-to-code generation. Similarly to most code models however, CodeGPT was trained on a limited set of widely-used languages (Java, Python) - leading to constrained efficacy in lower-resource languages. This motivated us to research CodeGPT's performance on the token-level code completion task across high- and low-resource languages. We investigate in which scenarios CodeGPT predicts incorrect tokens with high certainty using a tuned lens, followed by studying attention patterns that underlie the observed behaviour. Our findings indicate that CodeGPT is most competent in Java and Python code (Top-1 accuracies: 69.2% and 68.2% respectively). It generates false predictions with highest confidence when it encounters unfamiliar constructs in low-resource languages, or code structures that cannot be predicted from left context only. Moreover, we find a positive correlation between null attention and model confidence.