This framework provides a structured approach to evaluating AI code assistant agents based on real-world development scenarios. It's designed to help developers and teams assess which agent might best suit their specific needs and workflows.
| Dimension | Description | Evaluation Questions |
|---|---|---|
| Code Comprehension | Ability to understand existing codebases | How well does the agent understand large files, complex patterns, and project architecture? Does it accurately map dependencies and relationships? |
| Context Retention | Maintaining awareness across interactions | Can the agent remember previous discussions and decisions? How well does it maintain context across multiple operations? |
| API Accuracy | Correctness in working with APIs | Does the agent correctly implement API signatures? Does it fabricate non-existent APIs? How accurately does it interpret types and documentation? |
| File Management | Handling large files and codebases | How does the agent approach large files? Does it create backups? Does it implement changes incrementally to avoid conflicts? |
| Test-Driven Development | Working with and creating tests | Does the agent write tests before implementation? Can it understand test failures and fix code accordingly? |
| Dimension | Description | Evaluation Questions |
|---|---|---|
| Iteration Style | How the agent approaches problem-solving | Does the agent work incrementally? Does it review and optimize its solutions? How transparent is its problem-solving process? |
| Methodology Adaptation | Alignment with development practices | Can the agent follow TDD principles when asked? How easily does it adapt to your preferred development methodology? |
| Documentation Generation | Creating supporting documentation | How well does the agent document its proposed solutions? Does it maintain ROADMAP.md or similar artifacts to track progress? |
| Planning Capability | Structured approach to complex tasks | Does the agent plan before executing? Does it break down complex tasks into manageable steps? |
| Collaboration Support | Facilitating human-agent teamwork | How well does the agent document decisions for review? Can it pick up where it left off if interrupted? |
| Dimension | Description | Evaluation Questions |
|---|---|---|
| Speed | Time to completion | How quickly does the agent complete tasks? Is speed achieved at the expense of quality? |
| Token Efficiency | Optimal use of context window | How efficiently does the agent use its available context? Does it summarize previous work effectively? |
| Action Economy | Efficiency of operations | How many discrete actions does the agent need to complete a task? Does it solve problems with minimal steps? |
| Pricing Model | Cost structure and limitations | What are the usage limits? How does pricing scale with use? Are there hidden costs for certain operations? |
| Error Recovery | Handling of mistakes and limitations | How does the agent recover from errors? Does it create backups or patches automatically? |
| Dimension | Description | Evaluation Questions |
|---|---|---|
| Large File Handling | Managing substantial code changes | How does the agent approach refactoring files >1000 LOC? Does it have strategies for breaking down the work? |
| Progressive Implementation | Incremental approach to changes | Does the agent make changes incrementally? Does it test after each change? |
| Code Duplication Avoidance | Reusing existing patterns | Does the agent recognize and reuse existing patterns in the codebase? How often does it create duplicate functionality? |
| Impact Assessment | Understanding wider effects | Does the agent assess the impact of changes on other parts of the codebase? |
| Dimension | Description | Evaluation Questions |
|---|---|---|
| Requirements Adherence | Following specifications | How closely does the agent follow requirements? Does it add unnecessary features? |
| Integration Quality | Fitting into existing systems | How well does new code integrate with existing code? Does it maintain architectural consistency? |
| Edge Case Handling | Anticipating problem scenarios | Does the agent anticipate and handle edge cases? How thorough is its implementation? |
| Dimension | Description | Evaluation Questions |
|---|---|---|
| Problem Diagnosis | Identifying root causes | How accurately does the agent diagnose issues? Does it consider multiple potential causes? |
| Fix Implementation | Correctness of solutions | Are the fixes correct and comprehensive? Do they address root causes or just symptoms? |
| Regression Prevention | Avoiding new issues | Does the agent avoid introducing new bugs? Does it add tests to prevent regressions? |
The following prompting strategies can significantly impact agent performance across all dimensions:
| Technique | Description | Example Prompt |
|---|---|---|
| Pace Control | Slowing down the agent | "Let's take it slow and think through this problem step by step." |
| Documentation Request | Asking for written planning | "Before implementing, create a document outlining your approach with assumptions, solutions, and phases." |
| Iterative Guidance | Encouraging small steps | "Work in small batches and let's verify each change before moving to the next step." |
| Test-First Direction | Enforcing TDD | "Write tests first that capture the expected behavior before changing the implementation." |
| Self-Assessment | Prompting for reflection | "What specific information would you need to know before implementing these changes?" |
| Progress Tracking | Maintaining awareness | "After completing this phase, update our tracking document with what was completed, what was learned, and what's next." |