Analyzing AI Agent Performance on TypeScript Tasks: A Deep Dive
As AI coding agents become increasingly sophisticated, understanding their failure modes is crucial for advancing their capabilities. We're launching a research project to systematically analyze solution trajectories from the Multi-SWE benchmark, with a specific focus on TypeScript repositories—where AI agents struggle the most.
The Multi-SWE benchmark evaluates AI agents on real-world software engineering tasks, primarily pull requests that require understanding existing codebases and implementing targeted solutions. While agents show promising results across various programming languages, TypeScript and JavaScript consistently exhibit some of the lowest resolution rates. This performance gap presents both a challenge and an opportunity: by understanding why agents fail on TypeScript tasks, we can identify fundamental limitations in current approaches and develop more robust solutions.
Our research takes an empirical approach to this problem. Rather than theorizing about potential failure modes, we're conducting a detailed analysis of actual agent trajectories—the sequence of actions, decisions, and code modifications that agents make while attempting to solve tasks. This trajectory-level analysis allows us to see not just whether an agent succeeded or failed, but how and why specific approaches led to particular outcomes.
Research Objectives
Our analysis will focus on four key areas:
Root cause analysis of task failures
- We'll categorize the fundamental reasons agents fail to solve TypeScript tasks correctly, whether due to type system misunderstandings, incorrect dependency resolution, module system confusion, or other language-specific challenges.
Pattern recognition in incorrect solutions
- By identifying recurring patterns that lead to wrong solutions, we can develop targeted interventions and training approaches that address these systematic errors.
Loop detection and efficiency analysis
- We'll examine scenarios where agents enter unproductive loops, repeatedly attempting similar unsuccessful approaches, and identify what prevents them from finding alternative solution paths within reasonable computational budgets.
Solution trajectory optimization
- Beyond success and failure, we'll evaluate how efficiently agents search the solution space, comparing successful trajectories to identify characteristics of optimal problem-solving approaches.
The insights from this research will inform both the development of better AI coding agents and the creation of higher-quality training data. By understanding the specific challenges TypeScript presents to current systems, we can help bridge the gap between benchmark performance and real-world software engineering capabilities.
Interested in Collaborating?
We're always looking to partner with AI labs on research that advances the field.