# AI & Learning ### Post-Trade Evaluation * Analyze Decisions: Agents review why trades were made. * Outcome Review: Identify what worked and what failed. * Lessons Learned: Store insights for future trades. *** ### Knowledge Accumulation * Agent Journals: Every agent keeps a record of past trades and rationale. * Reference Past Learning: Agents use historical data to refine strategies. * Continuous Improvement: Strategies evolve over time based on performance. *** ### Adaptive Behavior * Dynamic Strategy Adjustments: Agents optimize themselves for changing markets. * Reduced Errors: Learning from past trades minimizes repeated mistakes. * Better Risk Management: Agents make smarter decisions using accumulated knowledge. *** ### Why It Matters * Smarter Agents: Each trade makes your AI more effective. * Time-Saving: Automated learning reduces manual strategy tweaks. * Edge in the Market: Adaptation ensures agents stay aligned with current conditions. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.getdig.ai/dig/dig-trade/ai-and-learning.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.