Frequently Asked Questions
Common Questions About AI Agent Memory Design
Everything you need to know before registering.
What are the three layers of AI agent memory covered in this workshop? +
This workshop covers three memory layers: working memory (the active context window for the current task), episodic memory (a compressed record of past interactions that can be selectively retrieved), and semantic memory (the embedded knowledge base accessed through the RAG pipeline). Designing these three layers to work together is what gives AI agents reliable long-term context without context window overflow.
How do I prevent AI agent memory from overflowing the context window? +
Context window overflow is prevented through active memory management: summarising and compressing episodic memory rather than retaining raw transcripts, using selective retrieval to pull only relevant memories into working memory, and setting explicit context budgets per agent that trigger compression when approaching the threshold. The workshop covers all three techniques with practical Python implementation.
What is the difference between short-term and long-term AI agent memory? +
Short-term AI agent memory is working memory: the current context window contents available for immediate use. Long-term memory is episodic and semantic memory: past interactions stored in compressed form and a knowledge base, both accessible through retrieval. The engineering challenge is moving information efficiently between these layers without losing important context or overwhelming working memory.
How does RAG function as part of an AI agent memory system? +
RAG serves as the retrieval interface to semantic memory. When an agent needs domain knowledge or past context not in its current working memory, it queries the RAG pipeline which retrieves relevant content from the embedded knowledge base. The retrieved content is injected into working memory as structured citation, keeping context relevant and verifiable.
Can AI agent memory persist across sessions? +
Yes. The episodic memory layer is designed to persist across sessions, storing compressed conversation summaries and key decisions in a retrievable format. When a new session begins, the memory system retrieves relevant episodic memories to give the agent appropriate context from past interactions. The workshop covers session persistence implementation for production systems.
How do I implement memory sharing between multiple agents safely? +
Memory sharing between agents requires explicit access controls and clear versioning to prevent one agent's writes from corrupting another's state. The workshop covers shared memory architecture using the MCP resource system, which provides typed, validated read and write access to shared memory stores. Each agent's access is logged by the Glass-Box layer making memory interactions auditable.
