PRAXIS: Graph-Grounded Tacit Knowledge for Domain Code Generation

• URL: http://arxiv.org/abs/2608.19784v1
• Date: Thu, 20 Aug 2026 08:28:28 GMT
• Title: PRAXIS: Graph-Grounded Tacit Knowledge for Domain Code Generation
• Abstract summary: We identify the root cause of the agent's lack of tacit knowledge, including domain-specific business rules, interface contracts, and operational conventions that developers internalize through practice but never document.<n>This knowledge is deeply buried beneath the domain code, across code entities and their dependency relations, and invisible to the agent that lacks it.<n>We propose PRAXIS, a framework that enables agents to extract, represent, and reuse tacit knowledge for domain code generation.
• Score: 61.752289169878566
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: LLM agents have achieved strong performance on general software engineering tasks, yet struggle with domain-specific code generation. We identify the root cause as the agent's lack of tacit knowledge, including domain-specific business rules, interface contracts, and operational conventions that developers internalize through practice but never document. This knowledge is deeply buried beneath the domain code, dispersed across code entities and their dependency relations, and invisible to the agent that lacks it. These properties make tacit knowledge inherently difficult to retrieve or learn. In this work, we propose PRAXIS, a framework that enables agents to systematically extract, represent, and reuse tacit knowledge for domain code generation. PRAXIS acquires tacit knowledge by simulating human development workflows within the target codebase, distills it into structured units organized on the code dependency graph, and proactively surfaces it to the agent at the point of code interaction. Extensive experiments demonstrate that PRAXIS outperforms state-of-the-art agents equipped with powerful agentic search capabilities, as well as experience-based and skill-based methods. The approach integrates seamlessly into various agent frameworks and LLMs with consistent performance improvements, and supports continual evolution with performance steadily scaling as practice accumulates.

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