How do you design an AI agent to extract customer emails with limited context?

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To design an AI agent for extracting customer emails with limited context, the initial step involves robust text preprocessing to clean and normalize input data, removing noise and standardizing formats. This is followed by leveraging regular expressions (regex), which are highly effective for identifying common email patterns like user@domain.com. For more ambiguous or unstructured scenarios, a Named Entity Recognition (NER) model, specifically trained on diverse contact information, can pinpoint potential email candidates within broader text segments by understanding their semantic role. Additionally, employing a fine-tuned large language model (LLM) becomes vital to infer email intent from surrounding context and sentence structure, even if the format deviates slightly from strict regex patterns. Post-extraction, a crucial validation step verifies that detected strings adhere to standard email syntax, often coupled with a confidence score to flag uncertain extractions. Finally, an iterative human-in-the-loop feedback mechanism is essential for continuous model refinement, adapting to new or evolving email patterns with limited context.