Others build agent memory, and what I took from each
The article discusses the challenges faced by AI agents in adapting to user preferences and styles. It highlights the importance of agent signals, which provide context about users to improve interactions. The author also compares different AI memory systems and their approaches to managing user signals.
- ▪AI agents often start from scratch in each interaction, failing to retain user preferences.
- ▪Agent signals are crucial for providing context to improve communication and responses.
- ▪Different AI memory systems, like ChatGPT and Claude Code, have unique approaches to managing user signals.
Opening excerpt (first ~120 words) tap to expand
Back to Notes May 21, 2026 How others build agent memory, and what I took from each By Apoorva Shete Starting from zero, every time An engineer at Falconer asks our agent what’s safe to change to ship a new payments retry path. They’ve owned this code for two years. The agent answers like they’re new to the codebase. It explains what the orchestration layer is, where the idempotency keys live, the basics of the retry queue. None of that was useful. A different user prefers tight bullet lists for meeting summaries. The agent returns three paragraphs of flowing prose. Another user hates em dashes in writing. Every draft the agent produces is laced with them. None of this is a knowledge problem. The agent can look things up.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Falconer.
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