Build a Persistent Memory Agent with Decay & Self‑Evaluation

In the first part of the tutorial we lay out the core architecture of a Persistent Memory & Personalisation system. The agent stores user‑specific data in a lightweight key‑value store, then uses a set of handcrafted rules to decide which pieces of information are relevant for a given context. By applying a decay function to each memory entry, the system automatically forgets stale facts, keeping the knowledge base fresh while still remembering long‑term preferences.

The second section dives into self‑evaluation loops. After generating a response, the agent compares it against a set of success metrics such as user satisfaction or response relevance. If the evaluation score falls below a threshold, the agent rewrites its reply or updates its memory entries. This continuous feedback mechanism gives the agent a simple form of learning without the need for gradient descent or large training corpora. We also showcase practical examples—how a chatbot can adapt its tone over months, how a personal assistant can refine task‑completion strategies, and how to plug the system into existing frameworks with minimal overhead.

By combining rule‑based persistence, decay, and self‑evaluation, the article demonstrates a pragmatic pathway toward building adaptive, agentic AI that feels more human over time. Developers can prototype quickly, iterate fast, and eventually layer more sophisticated models if desired—all while keeping the core logic transparent and maintainable.