Confucius Code Agent: Scalable Agent Scaffolding for Real-World Codebases

• URL: http://arxiv.org/abs/2512.10398v4
• Date: Wed, 17 Dec 2025 04:22:36 GMT
• Title: Confucius Code Agent: Scalable Agent Scaffolding for Real-World Codebases
• Authors: Zhaodong Wang, Zhenting Qi, Sherman Wong, Nathan Hu, Samuel Lin, Jun Ge, Erwin Gao, Wenlin Chen, Yilun Du, Minlan Yu, Ying Zhang,
• Abstract summary: We introduce the Confucius Code Agent (CCA), a scalable software engineering agent that can operate at large-scales.<n>CCA is built on top of the Confucius SDK, an agent development platform structured around three complementary perspectives.<n>In addition, we introduce a meta-agent that automates the synthesis, evaluation, and refinement of agent configurations.
• Score: 44.366968508477235
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Real-world software engineering tasks require coding agents that can operate over massive repositories, sustain long-horizon sessions, and reliably coordinate complex toolchains at test time. Existing research-grade agents offer transparency but struggle when scaled to real-world workloads, while proprietary systems achieve strong practical performance but provide limited extensibility, interpretability, and controllability. We introduce the Confucius Code Agent (CCA), a scalable software engineering agent that can operate at large-scale codebases. CCA is built on top of the Confucius SDK, an agent development platform structured around three complementary perspectives: Agent Experience (AX), User Experience (UX), and Developer Experience (DX). The SDK integrates a unified orchestrator with hierarchical working memory for long-context reasoning, a persistent note-taking system for cross-session continual learning, and a modular extension system for reliable tool use. In addition, we introduce a meta-agent that automates the synthesis, evaluation, and refinement of agent configurations through a build-test-improve loop, enabling rapid adaptation to new tasks, environments, and tool stacks. Instantiated with these mechanisms, CCA demonstrates strong performance on real-world software engineering tasks. On SWE-Bench-Pro, CCA reaches a Resolve@1 of 54.3%, exceeding prior research baselines and comparing favorably to commercial results, under identical repositories, model backend, and tool access. Together, the Confucius SDK and CCA form a general, extensible, and production-grade foundation for building effective and robust coding agents, bridging the gap between research prototypes and practical large-scale deployment.

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