𝗔𝗱𝗱𝗶𝗻𝗴 𝗠𝗲𝗺𝗼𝗿𝘆 𝘁𝗼 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁
Building an AI agent requires more than just a model. It needs a way to remember who you are.
In my series on building the AWS Briefing Agent, I am exploring how to move from stateless interactions to personalized experiences.
The problem with standard microVMs is that they are stateless. When a session ends, the environment is destroyed. This prevents data leakage, but it also means the agent forgets everything. Without memory, a user must repeat their preferences every single time they log in.
I solved this using AgentCore Memory and Strands Agents.
Here is how the architecture works:
- Short-term memory: Stores raw interactions for 90 days to maintain context during a chat.
- Long-term memory: Uses two specific strategies to turn conversations into lasting knowledge.
- Semantic Strategy: Extracts factual data, such as which AWS services a user uses in production.
- User Preference Strategy: Identifies styles and interests, like a preference for serverless tools.
To keep data safe, the system uses namespaces. By using the {actorId} variable, I ensure that one user's data never leaks into another user's session.
I also integrated AWS Cognito for secure authentication. The agent validates JWT tokens to verify identity. Once verified, the system extracts the unique 'sub' claim from the token. This ID becomes the actor_id, which the agent uses to fetch the correct memory records.
The integration is seamless. By using the AgentCoreMemorySessionManager, the agent automatically:
- Loads conversation history from short-term memory.
- Searches long-term memory for relevant facts and preferences.
- Injects that context into the agent state before responding.
The result is an agent that feels personal. It knows your expertise level and your specific AWS interests without you ever repeating yourself.
Source: https://dev.to/aws-heroes/adding-memory-to-the-agent-181k
Optional learning community: https://t.me/GyaanSetuAi