## Context Engineering: Analyzing One Million Emails A team recently shared their findings after analyzing over a million emails with the goal of transforming them into structured context usable for AI agents. The experience led to several key observations. ## Main Challenges Thread reconstruction proved more complex than expected, due to replies, forwards, participants joining mid-conversation, and decisions revised later on. Systems that simply concatenate text in chronological order often fail because they lose track of who said what and why it matters. Attachments, such as PDFs, contracts, and invoices, constitute an essential part of the conversation and require OCR (optical character recognition) and structural analysis capabilities to be interpreted correctly. Multilingual conversations are more common than you might think, especially in international teams. Semantic search optimized for English loses effectiveness when cross-language understanding is required. ## Privacy and Performance Data retention is a sensitive issue, and many enterprise customers require that no data be stored. The team chose to discard every prompt after processing, reconstructing memory on demand from the original sources. In terms of performance, the system achieves around 200ms for information retrieval and about 3 seconds for the generation of the first token, even with large mailboxes. Most of the time is spent in the reasoning phase, not in the search.