Knowledge retention is the foundation of AI Agent architecture But Memory can be very complex, Here's a simple guide that can help Memory is a rather complex structure to understand, It is not only a very important part of an AI Agent but rather a significant entity for any complex LLM-based workflow. It utilises various design principles and can be divided into 3 layers: 📌 Short-Term Memory (STM): - Action: Holds recent interactions and ensures contextual continuity within a single session. - Function: Processes current inputs, tracks ongoing conversations, and applies attention mechanisms to prioritise relevant information. - It has limited capacity, and older information is overwritten unless transferred to long-term memory. - A few types of Short-term memory are cache as well as working memory. 📌 Long-Term Memory (LTM): - Action: Stores structured information for future reference, including user preferences, past interactions, learned workflows, and domain-specific knowledge. - Function: Enables AI to recognize recurring patterns, recall past interactions, and personalize responses based on accumulated experiences. - Unlike STM, LTM is designed to persist, ensuring that AI agents do not start from scratch in every interaction. - A few well-known types of Long-Term Memory are Episodic Memory, Semantic Memory and Procedural Memory. 📌 Feedback Loops: - Content: Acts as the self-improvement mechanism of AI memory, refining both STM and LTM over time. - Function: Incorporates user feedback (explicit or implicit) to adjust memory structures, enhancing accuracy and relevance. - This process allows AI to improve continuously by reinforcing useful knowledge and discarding outdated or incorrect information. 📌 Example: - Short-Term Memory: A chatbot helps a user troubleshoot an internet issue in real-time, processing responses and focusing on relevant details. Once the session ends, the chatbot forgets the interaction unless the data is transferred to long-term memory. - Long-Term Memory: An AI agent remembers a patient's medical history and preferences, allowing it to schedule follow-up appointments efficiently and personalise the experience, resulting in faster scheduling and increased patient satisfaction. - Feedback Loop An AI agent in a manufacturing plant uses feedback loops to improve defect detection by tracking the accuracy of its decisions and adjusting its parameters over time, leading to improved quality control and reduced inspection time. If you are a business leader, we've developed frameworks that cut through the hype, including our five-level Agentic AI Progression Framework to evaluate any agent's capabilities in my latest book. 🔗 Book info: https://amzn.to/3G2hLwg Save 💾 ➞ React 👍 ➞ Share ♻️ & follow for everything related to AI Agents | 20 comments on LinkedIn