7.2 Enhancing Agent Capabilities with Interaction Data

Beyond improving the underlying LLM, user interaction data plays an irreplaceable role for the AI Agent as an integrated intelligent system, particularly in areas like task execution, planning, memory, and automated process improvement.

7.2.1 Task Execution and Planning Optimization

The core capability of an AI Agent lies in understanding user intent and autonomously planning and executing tasks. User interaction data provides the Agent with invaluable experience to learn from and optimize its decision-making process.

Trajectory Learning and Reflection Mechanisms: During task execution, an Agent generates a series of interaction trajectories, including user inputs, actions taken by the Agent (e.g., tool calls, internal thought steps), and the final task outcome. By recording and analyzing these trajectories, especially successful and failed cases, the Agent can learn how to more effectively decompose complex tasks, select appropriate tools, and optimize its internal planning strategies [5]. For example, methods like AgentTuning train models to generate more reasonable intermediate steps by analyzing trajectory data, thereby enhancing the Agent’s end-to-end task completion capabilities. This aligns with the concepts of Imitation Learning and Behavioral Cloning, where the Agent learns decision-making policies by observing the behavior of an “expert” (which could be a human user or a more advanced Agent).

Reinforcement Learning for Policy Optimization: By using task completion rates, user satisfaction scores, and other signals from interaction data as rewards, an Agent can learn to select optimal action sequences in complex environments through Reinforcement Learning (RL). For instance, in dialogue management, an Agent can learn when to ask clarifying questions to resolve user intent or when to invoke external functions to complete a specific task. This optimization process enables the Agent to better adapt to dynamic and uncertain environments, enhancing the robustness of its autonomous decision-making.

7.2.2 Memory and Personalized Services

To provide coherent and personalized services, an AI Agent needs effective memory capabilities. User interaction data is the foundation for building and updating the Agent’s memory module.

Short-term and Long-term Memory Construction: Interaction data contains users’ historical preferences, habits, and specific information (e.g., a “50-minute lesson” or a “jellyfish birthday card” mentioned in a conversation). This information can be extracted and stored in the Agent’s memory module to support personalized responses across sessions. Short-term memory focuses on the context of the current conversation, while long-term memory stores persistent user preferences and knowledge, ensuring the Agent can provide consistent and customized services at different points in time.

Coreference Resolution and Contextual Coherence: In multi-turn dialogues, users frequently use pronouns (e.g., “it,” “he”) or elliptical sentences. By analyzing the contextual links of these referential words in interaction data, the Agent can more accurately understand user intent, reduce ambiguity, and maintain conversational coherence. This is crucial for providing a natural and fluent conversational experience.

7.2.3 Automated Process Improvement

User interaction data is not only used to optimize the Agent’s intelligent core but also provides data support for the continuous improvement of the entire automated workflow.

Data Feedback Loop and Iterative Optimization: Collecting user feedback on Agent outputs (e.g., corrections, repeated questions) is key to continuously optimizing the Agent’s Prompt Engineering and task workflow design. By analyzing this feedback, the Agent’s weaknesses can be identified, and targeted improvements can be made. For example, generating diverse dialogue paths through methods like Monte Carlo simulation can enhance the Agent’s robustness in various complex situations.

Tool Calling and Error Correction: Agents often need to call external tools (e.g., APIs, databases) to perform tasks. Interaction data records the success and failure cases of the Agent’s tool calls. By analyzing the failure cases, the causes of errors can be diagnosed, and the Agent’s tool selection strategy, parameter configuration, or error handling mechanisms can be updated. This reduces execution errors in future tasks and improves the Agent’s reliability.