Build an Agentic AI with Persistent Memory & Personalization

In the age of conversational AI, users increasingly expect assistants to remember past conversations and adapt to their preferences. Traditional chatbots treat each turn as a stateless exchange, which limits personalization and context continuity. This tutorial demonstrates how to build an agent that not only stores and recalls user interactions but also evaluates its own responses and applies decay to older memories, mimicking the forgetting process observed in human cognition. By structuring the system around a simple rule‑based engine, developers can prototype persistent memory without the overhead of heavy learning frameworks, while still achieving meaningful personalization.

The core of the design is a lightweight memory module that records key-value pairs, where keys are contextual tags and values are user statements or preferences. When a new input arrives, the engine retrieves relevant tags, applies decay factors based on elapsed time, and scores potential responses using a self‑evaluation metric that compares the candidate output to an ideal response template. Rule sets govern whether to reinforce a memory, overwrite it, or discard it entirely. For example, if a user repeatedly asks for coffee suggestions, the system will strengthen the 'coffee preference' entry; if the user stops mentioning coffee for weeks, the entry decays and may be removed. This approach keeps the memory footprint bounded and ensures the agent’s behavior evolves organically.

Running the prototype in a simulated conversation loop shows how the agent’s replies gradually become more tailored—mentioning the user’s favorite brand, offering tailored recommendations, and even apologizing when a suggestion misses the mark. The self‑evaluation component flags low‑confidence responses, triggering a fallback to a generic answer or prompting the user for clarification. By the end of the tutorial, readers will have a fully functional agent that demonstrates persistent memory, personalization, decay, and self‑evaluation, and will understand how to extend the rule base to accommodate new domains or integrate with machine‑learning back‑ends for richer inference.