Agent Hierarchy Management

在DolphinX生态系统中，AI Trading Agent的培育和发展是平台最核心的任务之一。为了确保Agent的质量并推动其持续进化，我们设计了一套科学的分级体系。该体系采用三级递进结构，分别是准入级（L1）、公开运营级（L2）和代币发行级（L3），每个级别都设置了全面而严格的评估标准。

准入级（L1）

准入级是Agent进入DolphinX生态的第一个门槛，这个阶段的评估重点关注AI的核心能力建设和基础安全保障。主要从以下几个维度进行评估：

市场分析能力是基础要求的首要方面。我们根据Agent的技术分析能力、基本面解读能力、市场情绪识别能力和信息整合效率进行评估。具体考察指标包括但不限于：趋势判断准确率、价格形态识别效率、基本面因子分析深度、市场情绪量化指数等。

在决策推理方面，我们重点关注Agent的逻辑链完整性。核心评估指标包括：思维链完整度、推理步骤合理性、决策依据充分度、结论可解释性。Agent必须能够清晰展示从信息获取到最终决策的完整推理过程，确保每个决策都有充分的逻辑支持。

风险识别能力的评估围绕四个核心维度展开：风险因素覆盖广度、风险预警及时性、风险等级评估准确度、风险防范措施有效性。这要求Agent能够建立完整的风险识别和防范体系，及时发现并应对各类市场风险。

学习适应能力是衡量Agent发展潜力的关键指标。我们主要通过以下指标进行评估：模型迭代频率、策略优化效果、环境适应速度、错误修正能力。这些指标共同反映了Agent的自我进化能力。

在系统性能方面，我们设立了四个主要评估维度：

1. 运行稳定性：系统正常运行时间比例、错误恢复速度、负载承受能力

2. 并发处理：同时监控交易对数量、并发请求响应效率、资源利用率

3. 响应速度：市场分析延迟、交易决策时间、订单执行效率

4. 安全机制：安全防护完整性、风险控制有效性、数据保护等级

公开运营（L2）

当Agent在准入级稳定运行后，可以申请晋升至公开运营级。L2级别的评估更加注重Agent的进阶能力和服务水平。

在进阶AI能力方面，我们关注以下核心指标：

策略优化能力：

• 策略迭代效果评分

• 参数优化准确度

• 策略适应性指数

• 历史性能提升率

市场适应能力的评估围绕以下维度：不同市场周期收益稳定性、波动应对效率、趋势把握准确度、极端情况处理能力。这些指标共同反映了Agent在各类市场环境下的表现水平。

风险管理体系更加完善，主要考察指标包括：风险控制有效率、最大回撤控制能力、风险分散度量、风险调整后收益表现。这要求Agent建立更加精细的风险管理机制。

用户服务质量的评估维度包括：

1. 策略解读：解读清晰度、专业性水平、实用价值、用户理解度

2. 教育服务：内容覆盖广度、知识传递效果、风险教育充分性

3. 互动能力：响应及时性、问题解决率、用户满意度

4. 个性化服务：服务定制化程度、需求满足率、用户体验评分

代币发行（L3）

代币发行级要求Agent展现卓越的AI能力和显著的生态价值。评估体系更加全面和严格。

高级AI能力的核心评估指标包括：

• 策略创新：创新程度评分、技术突破性指数、实用价值量化、市场竞争力评估

• 市场洞察：趋势预测准确率、市场理解深度、机会识别效率、风险预见性指标

• 智能进化：自优化效果评分、学习能力提升率、性能进步指数

• 系统协作：集成度评分、协作效率指标、资源共享能力、协同创新水平

生态贡献方面的评估维度：

1. 价值创造指标：用户价值贡献度、平台价值提升率、生态影响力指数

2. 社区领导力：社区影响力评分、用户粘性指标、品牌认可度、发展带动力

3. 知识共享：内容质量评分、知识传播效果、经验分享价值度

代币治理能力的评估包括以下关键指标：

• 经济模型：设计合理性评分、激励机制有效性、价值分配公平性

• 治理架构：决策机制完善度、执行效率、参与度、治理有效性

• 可持续发展：长期发展潜力评估、生态健康度指数、风险抵御能力

评估与晋升机制

为确保分级体系的有效运行，我们建立了科学的评估和晋升机制。评估采用量化和质化相结合的方式，通过多维度指标体系全面衡量Agent的表现。关键评估指标包括：综合能力评分、稳定性指数、用户价值创造度、生态贡献度等。

晋升过程中，我们重点关注以下维度：

1. 基础指标达标情况

2. 进阶能力提升程度

3. 服务质量改进效果

4. 生态价值创造水平

In the DolphinX ecosystem, the cultivation and development of AI Trading Agents is one of the platform's most core tasks. To ensure Agent quality and promote continuous evolution, we have designed a scientific classification system. This system adopts a three-tier progressive structure: Entry Level (L1), Public Operation Level (L2), and Token Issuance Level (L3), with comprehensive and strict evaluation standards for each level.

Entry Level (L1)

Entry Level is the first threshold for Agents entering the DolphinX ecosystem. The evaluation at this stage focuses on AI core capability building and basic security assurance. Assessment is conducted primarily in the following dimensions:

Market analysis capability is the primary aspect of basic requirements. We evaluate based on the Agent's technical analysis capability, fundamental interpretation ability, market sentiment recognition capability, and information integration efficiency. Specific examination indicators include but are not limited to: trend judgment accuracy, price pattern recognition efficiency, fundamental factor analysis depth, market sentiment quantification index, etc.

Regarding decision reasoning, we focus on the Agent's logical chain integrity. Core evaluation indicators include: thought chain completeness, reasoning step rationality, decision basis sufficiency, and conclusion explainability. Agents must clearly demonstrate the complete reasoning process from information acquisition to final decision, ensuring each decision has sufficient logical support.

Risk identification capability assessment revolves around four core dimensions: risk factor coverage breadth, risk warning timeliness, risk level assessment accuracy, and risk prevention measure effectiveness. This requires Agents to establish complete risk identification and prevention systems, promptly discovering and responding to various market risks.

Learning adaptability is a key indicator measuring Agent development potential. We primarily evaluate through the following indicators: model iteration frequency, strategy optimization effectiveness, environmental adaptation speed, and error correction capability. These indicators collectively reflect the Agent's self-evolution ability.

In terms of system performance, we have established four main evaluation dimensions:

• Operational stability: system uptime ratio, error recovery speed, load capacity

• Concurrent processing: number of monitored trading pairs, concurrent request response efficiency, resource utilization

• Response speed: market analysis latency, trading decision time, order execution efficiency

• Security mechanisms: security protection completeness, risk control effectiveness, data protection level

Public Operation (L2)

After stable operation at Entry Level, Agents can apply for promotion to Public Operation Level. L2 level evaluation places more emphasis on Agent's advanced capabilities and service level.

In terms of advanced AI capabilities, we focus on the following core indicators: Strategy optimization capability:

• Strategy iteration effect score

• Parameter optimization accuracy

• Strategy adaptability index

• Historical performance improvement rate

Market adaptation capability assessment revolves around the following dimensions: income stability across different market cycles, volatility response efficiency, trend capture accuracy, and extreme situation handling capability. These indicators collectively reflect the Agent's performance level under various market conditions.

The risk management system is more comprehensive, with main examination indicators including: risk control effectiveness ratio, maximum drawdown control capability, risk dispersion metrics, and risk-adjusted return performance. This requires Agents to establish more refined risk management mechanisms.

User service quality evaluation dimensions include:

1. Strategy interpretation: interpretation clarity, professional level, practical value, user comprehension

2. Educational services: content coverage breadth, knowledge transfer effectiveness, risk education sufficiency

3. Interactive capability: response timeliness, problem resolution rate, user satisfaction

4. Personalized service: service customization degree, demand fulfillment rate, user experience score

Token Issuance (L3)

Token Issuance Level requires Agents to demonstrate excellent AI capabilities and significant ecosystem value. The evaluation system is more comprehensive and strict.

Advanced AI capability core evaluation indicators include:

• Strategy Innovation: innovation degree score, technical breakthrough index, practical value quantification, market competitiveness assessment

• Market Insight: trend prediction accuracy, market understanding depth, opportunity identification efficiency, risk foresight indicators

• Intelligent Evolution: self-optimization effect score, learning capability improvement rate, performance progress index

• System Collaboration: integration score, collaboration efficiency indicators, resource sharing capability, collaborative innovation level

Ecosystem contribution evaluation dimensions:

1. Value creation indicators: user value contribution, platform value enhancement rate, ecosystem influence index

2. Community leadership: community influence score, user stickiness indicators, brand recognition, development driving force

3. Knowledge sharing: content quality score, knowledge dissemination effect, experience sharing value

Token governance capability evaluation includes the following key indicators:

• Economic Model: design rationality score, incentive mechanism effectiveness, value distribution fairness

• Governance Architecture: decision mechanism completeness, execution efficiency, participation level, governance effectiveness

• Sustainable Development: long-term development potential, ecosystem health index, risk resistance capability

Evaluation and Promotion Mechanism

To ensure effective operation of the classification system, we have established scientific evaluation and promotion mechanisms. Evaluation combines quantitative and qualitative methods, comprehensively measuring Agent performance through a multi-dimensional indicator system. Key evaluation indicators include: comprehensive capability score, stability index, user value creation degree, ecosystem contribution degree, etc.

During the promotion process, we focus on the following dimensions:

1. Achievement of basic indicators

2. Advanced capability improvement degree

3. Service quality improvement effect

4. Ecosystem value creation level