Confucius Code Agent (CCA)

• CCA is a state-of-the-art open-source AI software engineer built on the Confucius SDK, integrating Agent, User, and Developer Experience for complex coding tasks.
• It employs a hierarchical memory system, persistent note-taking, and modular tool integration to optimize context management and streamline code operations.
• The automated meta-agent loop drives rapid agent synthesis and performance benchmarking, achieving leading results on standardized software engineering benchmarks.

The Confucius Code Agent (CCA) is an open-sourced AI software engineer designed to address the complexities of real-world, industrial-scale coding tasks. CCA is built atop the Confucius SDK, incorporating explicit design principles to optimize agent autonomy, human accessibility, and developer control. The framework introduces novel architectural components including a hierarchical memory hierarchy, persistent note-taking, modular tool integration, and an automated meta-agent engineering loop. CCA achieves state-of-the-art performance on standardized software engineering benchmarks, offering transparency, extensibility, and reproducibility for both research and production environments (Wang et al., 11 Dec 2025).

1. Confucius SDK: Three-Axis Design

The Confucius SDK serves as the foundational development platform for CCA, formalizing three complementary experience axes: Agent Experience (AX), User Experience (UX), and Developer Experience (DX). These axes are expressed as the design triple

$$\mathrm{SDK}\;:\;(\mathrm{AX},\,\mathrm{UX},\,\mathrm{DX})$$

• Agent Experience (AX) filters, structures, and distills context for the LLM, minimizing noise and maximizing actionable information across complex codebases.
• User Experience (UX) governs human-facing outputs such as readable logs, Markdown-formatted progress notes, live diffs, and failure explanations.
• Developer Experience (DX) provides modular APIs, observability, a trace UI, A/B evaluation dashboards, and a meta-agent playground for configuration and debugging.

CCA is instantiated to simultaneously maximize all three axes, ensuring a principled separation of concerns that eliminates prompt bloat, context overflow, and developer-user friction.

2. Orchestrator Loop and Memory Hierarchy

Central to CCA operation is the orchestrator loop:

$$\begin{aligned} &\textbf{Input:}\;\mathcal{P}_\text{sys},\;\mathcal{M}_0,\;\mathcal{E},\;\text{max\_iters}\ &\textbf{Initialize:}~\mathcal{M}\leftarrow \mathcal{M}_0,\quad i\leftarrow 0.\ &\textbf{while }i<\text{max\_iters}:\ &\quad i\leftarrow i+1,\ &\quad \text{LLM\_out} \;\leftarrow\;\mathrm{LLM}\bigl(\mathcal{P}_\text{sys},\,\mathrm{encode}(\mathcal{M})\bigr),\ &\quad \{a_k\}\leftarrow \mathrm{parse}(\text{LLM\_out}),\ &\quad\textbf{for each }a_k\textbf{ do}\ &\qquad (\Delta\mathcal{M},\,\mathrm{artifacts})\leftarrow \mathcal{E}[a_k].\mathrm{execute}(a_k),\ &\qquad \mathcal{M}\leftarrow \mathcal{M}\cup \Delta\mathcal{M},\ &\quad\textbf{if }\{a_k\}=\varnothing\;\textbf{then break},\ &\textbf{return }(\mathcal{M},\,\mathrm{artifacts}). \end{aligned}$$

Here, $\mathcal{M}$ is the working memory, updated by a set of modular extensions $\mathcal{E}$. The hierarchical memory structure is defined as:

$$\mathcal{M} = \mathcal{M}_\text{session} \supset \mathcal{M}_\text{entry} \supset \mathcal{M}_\text{runnable}$$

• Session memory: spans multiple files and user turns.
• Entry memory: groups related task artifacts.
• Runnable memory: captures the state of individual tool/command executions.

When the serialized memory $\ell(\mathcal{M}_\text{serialized})$ approaches a threshold $T$, an Architect LLM call compresses historical messages into a structured summary $s$, ensuring $\ell(\mathrm{encode}(\mathcal{M})) \leq T$ without semantic loss.

3. Persistent Note-Taking and Continual Learning

Cross-session continual learning is enabled by a persistent note-taking subsystem. Every trajectory (user queries, LLM-generated actions, tool executions, system events) is logged as a Markdown corpus tree:

$$\mathcal{N} = \{(p_i, f_i)\},\quad p_i \in \mathrm{Paths},\, f_i \in \mathrm{Markdown} \cup \{\varnothing\}$$

Directories correspond to sessions, and leaf files contain structured annotations (successes, failures, TODOs, configs). Retrieval is accomplished by typed queries:

$$\mathrm{Find}(\mathcal{N},\,\texttt{error\_regex} = \gamma) \mapsto \{f_j \mid f_j \text{ mentions } \gamma\}$$

Upon session start, CCA pre-populates memory with past notes, yielding measurable gains in token efficiency ($-11$k tokens/session) and an observed Resolve@1 increase of $+1.4\%$ on SWE-Bench-Pro. This suggests persistent note-taking is critical for agent adaptation and generalization across tasks.

4. Modular Extensions and Tool Abstractions

External capabilities are implemented as modular extensions $\mathcal{E} = \{E_1, ..., E_K\}$, each exposing API hooks:

• on_input_messages: alters prompts preceding LLM invocation.
• on_tag/on_plain_text: parses LLM outputs into actionable tasks.
• execute: runs sandboxed operations, returning state artifacts.

Standard extensions include file edit diffs, shell/Bash execution (with safety validation), code search (grep/BigGrep), test runners, and plan/think modules. At inference, the orchestrator invokes extensions in a deterministic sequence, parsing outputs, executing actions, and reintegrating results as new memory nodes.

5. Automated Meta-Agent and Agent Engineering Loop

Agent synthesis, evaluation, and optimization are delegated to a meta-agent $A_\mathrm{meta}$, which automates the “build–test–improve” cycle for agent configurations:

$$C = (\mathcal{P},\,\mathcal{E},\,\mathcal{M}_\text{policy},\,\mathcal{T})$$

where $\mathcal{P}$ is the prompt template, $\mathcal{E}$ the extension set, $\mathcal{M}_\text{policy}$ memory compression parameters, and $\mathcal{T}$ the test suite. The loop executes:

$$\begin{aligned} &\text{Input: spec } S,\; \text{benchmarks } \mathcal{B}\ &1.\quad C_0 \leftarrow A_\mathrm{meta}.\mathrm{synthesize}(S)\ &2.\quad \text{repeat for } j = 0, 1, 2, \dots:\ &\quad\quad \text{results}_j \leftarrow \mathrm{Eval}(C_j\,|\,\mathcal{B})\ &\quad\quad \text{if metrics}(\text{results}_j) \geq \text{targets} \textbf{break}\ &\quad\quad \Delta_j \leftarrow A_\mathrm{meta}.\mathrm{proposePatch}(C_j,\text{results}_j)\ &\quad\quad C_{j+1} \leftarrow \mathrm{apply}(C_j, \Delta_j)\ &3.\quad \textbf{return } C_j \end{aligned}$$

This mechanism allows rapid adaptation to new benchmarks, environments, or tool stacks without manual agent reconfiguration. A plausible implication is an acceleration in agent development cycles, particularly in enterprise-scale codebases.

6. Benchmark Performance and Ablation Insights

CCA achieves state-of-the-art Resolve@1 performance on SWE-Bench-Pro, defined as:

$$\mathrm{Resolve@1} = \frac{1}{N} \sum_{i=1}^N \mathbf{1}(\text{patch}_i \text{ passes all tests})$$

Key results:

Backbone	Scaffold	Resolve@1 (%)
Claude 4 Sonnet	SWE-Agent	42.7
Claude 4 Sonnet	CCA	45.5
Claude 4.5 Sonnet	Live-SWE-Agent	45.8
Claude 4.5 Sonnet	CCA	52.7
Claude 4.5 Opus	Prop. Scaffold	52.0
Claude 4.5 Opus	CCA	54.3

Ablation studies on context management and learned tool-use (100-task subset) reveal improvements up to 6.6% in Resolve@1 when advanced context management is enabled, and persistent note-taking yields further performance gains on subtasks. Scaffolding and modular abstraction are found to be as critical as model scale for reliable agent performance.

7. Deployment Properties and Lessons Learned

CCA is characterized by full transparency, extensibility, and reproducibility:

• Transparency: All prompt templates, tool integration code, orchestrator logic, and memory policies are available open-source.
• Extensibility: New repositories and tools are incorporated through a stable extension API.
• Reproducibility: The trace UI records all actions for replay, ablation, or debugging.

Key deployment lessons:

1. Optimized scaffolding can outweigh the benefits of larger models.
2. Explicit separation of AX, UX, and DX is essential to avoid systemic inefficiencies and collaboration friction.
3. Automated meta-agent loops are particularly valuable for maintaining agent effectiveness in dynamic, enterprise contexts.

Together, the Confucius SDK and CCA demonstrate that rigorous, principled engineering across agent autonomy, context management, modularity, and self-improvement mechanisms can satisfy the demands of industrial-scale AI-driven software engineering while supporting transparent, extensible, reproducible research and production workflows (Wang et al., 11 Dec 2025).