Frequently Asked Questions
Common Questions About Fixing AI Agents That Lose Context
Everything you need to know before registering.
Why do AI agents lose context in long conversations? +
AI agents lose context in long conversations for two reasons: fixed context window size means older content gets pushed out as new content arrives, and there is typically no memory management system that compresses and stores important past context for retrieval when needed. Without explicit memory engineering, the agent's effective memory is limited to the most recent portion of the conversation.
How does episodic memory prevent context loss in long conversations? +
Episodic memory stores compressed records of past conversation turns in a retrievable format. When a new turn references an earlier topic, the memory manager retrieves the relevant episodic memory and injects it into the working context. This gives the agent access to past context without keeping the full conversation history in the context window. The workshop implements episodic memory as a production-ready component of the Glass-Box Context Engine.
What conversation information should be stored in episodic memory versus working memory? +
Working memory contains the current active context: the semantic blueprint, recent conversation turns, and current RAG retrievals. Episodic memory stores important decisions, user preferences, task outcomes, and key facts established in past turns. The memory manager decides what to move from working to episodic memory based on recency and importance scoring.
How do I make AI agents remember user preferences across sessions? +
User preferences stored as structured records in episodic memory can be retrieved at the start of each session. The memory manager retrieves relevant preferences based on the conversation topic and injects them into the working context through the semantic blueprint. This gives the agent appropriate personalization without requiring the user to re-specify preferences in every interaction.
Can AI agents maintain context across multiple sessions, not just within one conversation? +
Yes. Session-persistent episodic memory is a core feature of the three-layer memory architecture. The workshop covers implementing session persistence so agents can retrieve relevant context from past sessions: user history, previous task outcomes, established facts at the start of new sessions.
How do I test that my AI agents maintain context correctly over long conversations? +
The workshop covers long-conversation testing patterns: scripted test conversations with known context dependencies that should be retained, memory retrieval verification tests that check episodic memory contents after compression, cross-session consistency tests that verify preferences and facts persist correctly, and context coherence tests that check for contradictions between early and late conversation turns.
