Your AI Agents can reason across complex problems, call tools and generate convincing answers—but the moment a request ends the agent forgets everything. Agentic memory is not just storage, it's a context that actively changes an agent’s behavior.
This talk explains what agent memory really is, why it must be built separately from the model and why adding it is more complicated than simply storing chat history.
Based on one of my projects - [https://github.com/msalanci/logs_talk_to_me/tree/v3] - a conversational agent investigating AWS logs using SQL queries and AWS API calls - we will explore the that matter in practical systems: short-term conversation history (running locally), along with AWS Bedrock AgentCore memory (Semantic for reusable facts, Episodic for repeated experience and Summary for session summation).
We will also look at the more dangerous side of memory, when a fact remembered from an earlier conversation was injected into an unrelated session, agent trusted it, changed the SQL it generated, and caused the query to fail. A memory that was correct once had become misleading context.
Give your agent a memory, but save less, retrieve narrowly, and trust deliberately.

no special requirements