[Paper] Confucius Code Agent: An Open-sourced AI Software Engineer at Industrial Scale

Source: arXiv - 2512.10398v1

Overview

The Confucius Code Agent (CCA) is an open‑source AI “software engineer” that can handle massive codebases, long‑running sessions, and complex toolchains typical of real‑world development teams. Built on the newly released Confucius SDK, CCA demonstrates that a transparent, extensible agent can match (and even surpass) the performance of proprietary coding assistants on industrial‑scale benchmarks.

Key Contributions

• Confucius SDK: A unified platform that separates Agent Experience (AX), User Experience (UX), and Developer Experience (DX), making it easy to plug in new tools, memories, and evaluation loops.
• Hierarchical Working Memory: Enables the agent to reason over very long contexts (hundreds of thousands of tokens) without losing relevance.
• Persistent Note‑Taking System: Stores “notes” across sessions, giving the agent continual learning capabilities without retraining the underlying model.
• Modular Extension Module: Provides a clean API for integrating arbitrary development tools (e.g., linters, test runners, CI pipelines).
• Meta‑Agent Build‑Test‑Improve Loop: Automatically synthesizes, evaluates, and refines agent configurations, accelerating the creation of task‑specific agents.
• State‑of‑the‑Art Performance: Achieves 54.3 % Resolve@1 on SWE‑Bench‑Pro, a sizable jump over previous open‑source coding agents.

Methodology

1. Agent Architecture – CCA runs on top of a large language model (LLM) that is wrapped by the Confucius SDK orchestrator. The orchestrator manages three memory layers:

   • Short‑term working memory for the current prompt.
   • Hierarchical long‑term memory that chunks and indexes past interactions, allowing the agent to retrieve relevant code snippets or design decisions from millions of tokens of history.
   • Persistent notes that survive across independent sessions, acting like a lightweight knowledge base.

2. Tool Integration – The SDK defines a Tool Interface (input schema, execution sandbox, output parsing). Developers can drop in any CLI‑based tool (e.g., git, docker, static analyzers) without touching the core agent logic.

3. Meta‑Agent Loop – A separate “meta‑agent” treats the configuration of CCA (memory size, tool selection, prompting style) as a hyper‑parameter search problem. It iteratively:

   • Build a candidate configuration.
   • Test it on a held‑out set of coding tasks.
   • Improve by applying reinforcement‑style feedback (reward = task success, penalty = tool failures).

4. Evaluation – The authors benchmarked CCA on SWE‑Bench‑Pro, a collection of real‑world software engineering problems that require multi‑step reasoning, test generation, and bug fixing. Metrics focus on Resolve@k (the fraction of problems solved within the top‑k generated solutions).

Results & Findings

• Long‑context reasoning: The hierarchical memory reduced “context loss” by ~30 % compared with flat context windows.
• Cross‑session learning: Persistent notes boosted success on repeat‑type tasks by ~12 % without any model fine‑tuning.
• Extensibility: Adding a new static analysis tool required <30 lines of SDK‑compliant code and yielded immediate performance gains on relevant tasks.

Practical Implications

• Developer Productivity: Teams can deploy CCA as an internal “pair programmer” that remembers project conventions, past refactorings, and architectural decisions across weeks of work.
• CI/CD Integration: Because tool usage is modular, CCA can be wired into existing pipelines to automatically generate patches, run tests, and submit PRs—all under audit‑able logs.
• Cost‑Effective Scaling: Being open‑source, organizations avoid the per‑token fees of commercial agents while still getting comparable (or better) performance on large codebases.
• Custom Toolchains: Companies with proprietary linters, security scanners, or domain‑specific generators can plug them into the SDK without rewriting the agent core.
• Rapid Prototyping: The meta‑agent’s build‑test‑improve loop lets product teams experiment with new prompting strategies or tool combos in hours instead of weeks.

Limitations & Future Work

• Model Dependency: CCA’s gains assume access to a strong underlying LLM; performance will degrade with smaller, less capable models.
• Memory Overhead: Hierarchical indexing incurs additional storage and compute cost, which may be prohibitive for extremely constrained environments.
• Evaluation Scope: Benchmarks focus on single‑language (mostly Python/Java) tasks; broader language coverage remains to be validated.
• Security & Sandboxing: While the SDK provides execution sandboxes, fully guaranteeing safe execution of generated code in production still requires careful engineering.

Future directions include extending the SDK to multi‑modal agents (e.g., code + design diagrams), optimizing memory indexing for edge devices, and open‑sourcing a lightweight “distilled” version of CCA for hobbyist developers.

Authors

• Zhaodong Wang
• Zhenting Qi
• Sherman Wong
• Nathan Hu
• Samuel Lin
• Jun Ge
• Erwin Gao
• Yining Yang
• Ben Maurer
• Wenlin Chen
• David Recordon
• Yilun Du
• Minlan Yu
• Ying Zhang

Paper Information

• arXiv ID: 2512.10398v1
• Categories: cs.CL, cs.AI, cs.LG, cs.SE
• Published: December 11, 2025
• PDF: Download PDF