@hackproduct9: 4 levels of agent memory — "Book it." Book what? Your agent forgot again. 🧠 Each level remembers one more thing. The model remembers nothing at any level. 1️⃣ Stateless — the model alone. It forgets after every reply. → Perfect for: one-shot tasks. 2️⃣ History — add past turns to the context window. → Perfect for: a single conversation. Works until the window fills up. 3️⃣ Long-term memory — save facts outside the model, fetch them back. "Prefers aisle" survives a new session. → Perfect for: assistants people come back to. 4️⃣ Skills — save how-tos, not just facts. Prefs, last trip, booking skill: done within policy. → Perfect for: agents that should get better at the job over time. 👀 The part nobody says: every step up is code you write. What to save, what to fetch, what to forget. Agent memory is retrieval you built yourself. Rule: nothing, history, saved facts, saved skills. Add the next level only when users feel the gap. Where teams go wrong: dumping everything into one vector store. Preferences, past trips and rules all compete for the same top-5, and the policy check loses. 📸 Screenshot the last frame — all four levels. Save it before your next agent design review. Follow @hackproduct — we turn scary AI-engineering concepts into things you can actually ship. ⚡ . . #AIengineering #machinelearning #softwareengineering #LLM #AIagents

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Wednesday 07 October 2026 02:53:19 GMT