Confucius SDK: AI Agent Development Scaffold

• Confucius SDK is an open-sourced agent development scaffold that enables building and deploying AI software engineering agents with a modular, multi-perspective architecture.
• It integrates unified orchestration, hierarchical working memory, persistent note-taking, and automated agent synthesis to ensure scalability, reproducibility, and extensibility.
• Empirical evaluations reveal measurable performance improvements in industrial-scale deployments, with metrics such as Resolve@1 demonstrating its practical impact.

Confucius SDK is an open-sourced agent development scaffold for the construction and deployment of AI software engineering agents at industrial scale. It underpins the Confucius Code Agent (CCA), an AI software engineer capable of reasoning over massive codebases, maintaining long-term context, and robustly coordinating complex toolchains. The platform is architected around distinct axes—Agent Experience (AX), User Experience (UX), and Developer Experience (DX)—and introduces novel mechanisms for unified orchestration, hierarchical working memory, persistent note-taking for continual learning, modular extension integration, and automated agent synthesis and improvement. By addressing scalability, transparency, extensibility, and reproducibility concerns, Confucius SDK bridges the gap between research-grade agent prototypes and production-level systems (Wang et al., 11 Dec 2025).

1. Foundational Principles and Perspectives

Confucius SDK structures agent construction and execution around three complementary perspectives:

• Agent Experience (AX): Optimizes the information presented to LLM components for reasoning, ensuring concise, structured working memory, precise tool affordances, and undistracted long-context planning.
• User Experience (UX): Prioritizes clear, actionable feedback, including streaming diffs, human-readable traces, and operational safeguards, thereby fostering trust and steerability for human operators.
• Developer Experience (DX): Facilitates agent extensibility, configuration, and introspection via modular interfaces (prompts, tools, memory) and comprehensive visualization and evaluation support.

This tripartite design prevents context overload in AX, opacity in UX, and code path entanglement in DX, enhancing performance and maintainability.

2. Unified Orchestration and Memory Hierarchy

Central agent behavior in Confucius SDK is governed by a minimal, unified orchestrator loop parameterized by hierarchical working memory, modular extensions, and iteration-control policies. The loop is formalized as follows:

initialize session_context, memory = HierarchicalMemory() load all registered extensions for iter in 1...MaxIters: prompt = system_prompt + memory.compose_for_AX() llm_output = LLM(prompt) actions = parse(llm_output) for a in actions: ext = extension_router(a.type) obs = ext.execute(a) memory.update_with(obs) if ext.requests_continuation(): continue # Next iteration if no actions remain: break return final artifacts

HierarchicalMemory organizes agent state into named scopes:

• session_scope: all-time high-level insights
• entry_scope: per-task summaries
• runnable_scope: tool-call or code-run summaries

When the prompt length $L$ exceeds $L_{max}$, the built-in "Architect" module compresses historical memory using an LLM summarizer:

$$\text{if } L \geq L_{max} \Rightarrow \text{summary} \leftarrow \text{ArchitectLLM.summarize(history)} \ \text{memory.replace(span\_of\_history, summary)}$$

This maintains agent reasoning continuity while bounding context window size. Formally,

$$\text{memory\_content}(t) = C(H(t-\Delta)) \cup H(t-\Delta+1, \ldots, t)$$

where $H(t)$ is message history and $C$ denotes compression.

3. Persistent Note-Taking and Cross-Session Learning

Cross-session continual learning is achieved with a dedicated Note-Taker agent that asynchronously distills every agent “trajectory” (inputs, actions, outputs, errors) into a tree of typed Markdown nodes:

• Each node carries {id, title, keywords, body}.
• Structured fields: Problem, Solution, Insights.
• Update rule:

notes = NoteTaker(distill(trajectory)) for n in notes: write_or_merge(NodeTree, n)

The agent accesses accumulated knowledge by querying notes via APIs (e.g., retrieve_notes_by_keyword(error_message)), surfacing relevant historical fixes and solutions.

4. Modularity and Extension Mechanism

Toolchains (file editing, shell, code search, testing) integrate via the Extension module architecture:

• on_input_messages: modifies prompt pre-LLM call.
• on_llm_output: parses output into executable actions.
• on_execute: runs action, captures output.
• on_post: records outcomes into memory/artifacts.

Dynamic routing allows transparent addition, swap, or removal of extensions without modifying orchestration logic, facilitating robust interaction with diverse corporate tool stacks.

5. Automated Agent Synthesis: The Meta-Agent

Automated agent configuration is driven by the Meta-Agent, which iteratively synthesizes, tests, and refines agent setups based on build–test–improve loops:

spec = user_nl_specification best_config = None while not converged: candidate = MetaAgent.synthesize_config(spec) results = evaluate(candidate, regression_suite) feedback = analyze(results) spec = MetaAgent.refine_spec(spec, feedback) if meets_targets(results): best_config = candidate break return best_config

Each candidate is launched in the standard orchestrator runtime, evaluated on suite tasks, and incrementally improved (prompt updates, extension tweaks) without manual tuning, accelerating adaptation to new domains and requirements.

6. Empirical Evaluation and Industrial Scalability

Experiments utilize the SWE-ReX Docker runtime and the SWE-Bench-Pro benchmark with Anthropic Claude backbone variants. The key metric, Resolve@1, is defined:

$\text{Resolve@1} = \frac{1}{N} \sum_{i=1}^N s_i$

where $s_i \in \{0,1\}$ indicates success on task $i$ at first attempt.

Backbone	Previous Top Agent	CCA Performance
Claude 4.5 Opus	52.0% (proprietary)	54.3%
Claude 4.5 Sonnet	45.8% (Live-SWE-Agt)	52.7%
Claude 4 Sonnet	42.7% (SWE-Agent)	45.5%

Gains are attributed to orchestration, context management, and learned tool conventions within the Confucius SDK—not model scale.

Industrial-scale adaptations address:

• Massive codebase support (multi-million LOC, cross-module dependencies).
• Session persistence exceeding LLM context windows.
• Heterogeneous toolchain integration.

Engineering trade-offs include latency for context compression, asynchronous note aggregation, and performance stability for multi-file diffs (44.4% resolve rate for “10+ files”).

7. Transparency, Extensibility, Limitations, and Future Work

Confucius SDK’s open-source nature enables:

• Full transparency (prompts, tool policies, memory semantics).
• Typed extensions for domain-specific integration.
• Reproducible evaluation and ablation.

Limitations include:

• Incomplete fine-grained dependency tracking across refactors.
• Optimization opportunities for local vs. large-scale code search coordination.

Proposed future directions:

• End-to-end RL over AX trajectories with note/tool trace-derived reward signals.
• Online policy updates or hybrid on-device fine-tuning.
• Expansion of the Meta-Agent to maintain a workflow template catalog (e.g., release management, security, data pipelines).

The modular, multi-perspective architecture of Confucius SDK represents a trajectory for AI agents that are interpretable, adaptable, and scalable in enterprise software development contexts (Wang et al., 11 Dec 2025).