Improving Agents is a Data Mining Problem

Continual Learning, Harness Engineering, and Post-Training focus on curating data at scale to enhance and improve AI agents by running experiments. This approach was discussed at the AI Engineer World Fair, where the importance of data mining from Traces was highlighted as a crucial tool for companies to understand and improve their agents. Continual Learning involves agents acting in their environment and reintegrating the information gained back into the system, akin to human learning. Traces, which are projections of agent experiences, serve as valuable data to mine for understanding agent behavior. As agents become more complex and produce larger volumes of data, specialized systems like LangSmith Engine have been developed to efficiently process and analyze these traces, finding signals and issues, generating code fixes, and storing crucial information. The integration of open models, which are cost-effective and intelligent, allows for better processing of this data. A practical recipe for agent improvement involves using a combination of Harness Engineering and Fine-Tuning, allowing teams to iteratively enhance agent performance through data collection, evaluation, and continuous experimentation. This iterative process is essential for adapting to increasing data production and enhancing agent capabilities over time.

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