LLM-based agents can recover from individual execution errors, yet they repeatedly fail on the same fault when the underlying process knowledge--operator schemas, preconditions, and constraints--remains unrepaired. Existing self-evolving approaches address this gap by updating prompts, memory, or model weights, but none directly repair the symbolic structures that encode how tasks are executed, and few provide the governance guarantees required for safe deployment. We introduce ANNEAL, a neuro-symbolic agent that converts recurring failures into governed symbolic edits of a process knowledge graph without modifying foundation model weights. Its core mechanism, Failure-Driven Knowledge Acquisition (FDKA), localizes the responsible operator, synthesizes a typed patch through constrained LLM generation, and validates the proposal via multi-dimensional scoring, symbolic guardrails, and canary testing before commit. Every accepted edit carries full provenance and deterministic rollback capability. Across four domains and 27 multi-seed runs, ANNEAL is the only evaluated system that commits persistent structural repairs--strong baselines such as ReAct and Reflexion achieve high episodic recovery yet retain 72--100% holdout failure rates on recurring faults, whereas ANNEAL reduces these to 0% in the tested recurring-failure settings. Ablation confirms that removing FDKA eliminates all structural repairs and drops success rate by up to 26.7 percentage points. These results suggest that governed symbolic repair offers a complementary paradigm to weight-level and prompt-level adaptation for persistent fault elimination.

翻译：基于LLM的智能体可以从单个执行错误中恢复，但当底层过程知识（操作符模式、前置条件和约束）未得到修复时，它们会在同一故障上反复失败。现有自进化方法通过更新提示、记忆或模型权重来弥补这一不足，但均未直接修复编码任务执行方式的符号结构，且很少有方法提供安全部署所需的治理保证。我们提出ANNEAL——一种神经符号智能体，它将反复出现的失败转化为对过程知识图谱的受控符号编辑，而无需修改基础模型权重。其核心机制——失败驱动知识获取（FDKA）——定位负责操作符，通过受约束的LLM生成合成类型化补丁，并通过多维评分、符号护栏和金丝雀测试验证提案后提交。每个接受的编辑都带有完整的溯源信息和确定性回滚能力。在四个领域和27次多种子运行中，ANNEAL是唯一提交持久结构修复的评估系统——强基线方法如ReAct和Reflexion虽能达到高情节恢复率，但在反复故障上仍保留72%-100%的保留失败率，而ANNEAL在测试的反复失败设置中将其降至0%。消融实验证实，移除FDKA会消除所有结构修复，并使成功率下降高达26.7个百分点。这些结果表明，受控符号修复为权重级和提示级自适应提供了一种互补范式，以实现持久故障消除。