Multi-Agent AI Debugging Workflow for Focused Code Context
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Solution Overview
Problem
Existing generative AI models struggle to effectively debug complex coding issues when provided with excessive or irrelevant source code information, leading to a loss of focus and inefficient debugging processes.
Innovation Solution
A multi-agent workflow is implemented, utilizing a computing apparatus that orchestrates a collaborative agent and context retrieval agent to engage in a multi-turn conversational exchange with the user, retrieving contextual information and prompting the generative AI model to resolve coding issues efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the user provides lengthier portions of source code to the generative AI model, then the model can understand the code better, but the model becomes overwhelmed and loses focus on the problem
Solution Approach 1:
The patent segments the source code into relevant and irrelevant portions by using a multi-agent workflow where a context retrieval agent selectively extracts only the necessary code context around the exception, rather than providing the entire codebase. This segmentation allows the generative AI model to receive focused, manageable code snippets that maintain understanding while preventing overload.
Solution Approach 2:
The context retrieval agent extracts only the essential code context needed for debugging from the larger codebase. By taking out only the relevant portions (such as code around the exception location and immediate dependencies), the system eliminates irrelevant information that would overwhelm the model while preserving the necessary context for effective debugging.
2Loss of information
If the user provides the entire source code of a large software application, then the model has complete information, but the focus is diluted and the model becomes sidetracked by irrelevant parts
Solution Approach 1:
The patent applies local quality by providing the generative AI model with code context that is locally relevant to the exception location. The context retrieval agent identifies and provides only the local code surrounding the exception, making the code quality and relevance concentrated where it matters most, rather than distributing information uniformly across the entire codebase.
Solution Approach 2:
The context retrieval agent acts as an intermediary between the user's complete codebase and the generative AI model. This intermediary selectively translates and extracts only the necessary code information, filtering out irrelevant parts while maintaining the essential context needed for accurate debugging, thus preventing model distraction.
3Productivity
If the user determines what information to feed the model, then the model receives targeted data, but the process is not dissimilar to the coding process itself and is time-consuming
Solution Approach 1:
The context retrieval agent performs self-service by automatically analyzing the codebase and selecting relevant code snippets without requiring user intervention. The agent independently determines what code context is needed for debugging, eliminating the need for the user to manually filter and select information, thus saving time while maintaining targeted data delivery to the model.
Solution Approach 2:
The context retrieval agent performs preliminary action by pre-processing the codebase to identify and extract relevant code context before the generative AI model receives the information. This preliminary extraction and organization of code snippets reduces the burden on the user and accelerates the debugging process by having ready-to-use, filtered code context.
Data Source
AI summary
Systems, methods, and software are disclosed herein for resolving coding issues via generative AI integrations in various implementations. In an implementation, in a debugging session, a computing apparatus receives a user query relating to an exception in source code. The computing apparatus elicits a response from a generative AI model which is tasked with identifying an interaction pattern for resolving the user query. The computing apparatus mediates the debugging session according to the interaction pattern identified by the generative AI model.