Frequently Asked Questions
Common Questions About AI Agent Memory Management
Everything you need to know before registering.
What are the three types of memory that AI agents need to manage? +
Production AI agents need three memory types: working memory (the active context window for the current invocation, managed through semantic blueprint templates and context budgets), episodic memory (a compressed, retrievable record of past interactions, managed through a persistent store with importance scoring and TTL-based eviction), and semantic memory (the embedded knowledge base accessed through the RAG pipeline, managed through incremental indexing and retrieval confidence scoring). Each type requires different management techniques.
How do I implement working memory management for AI agents? +
Working memory management for AI agents uses a context budget allocator that divides the available context window tokens among content categories: a fixed allocation for the semantic blueprint, a dynamic allocation for RAG-retrieved knowledge (sized based on query complexity), a capped allocation for conversation history (the most recent N turns), and a reserve for the agent's response. The allocator assembles the context package and truncates or compresses any category that exceeds its allocation before agent invocation.
How does episodic memory compression work without losing important context? +
Episodic memory compression uses an importance-weighted summarisation approach: the memory manager scores each past interaction segment by recency, explicit importance flags (facts, decisions, user preferences), and relevance to the current session topic, then summarises the lower-importance segments while retaining the higher-importance segments at full detail. The compressed memories are stored with their importance scores, allowing the retrieval layer to surface the most relevant episodic context at any future invocation.
How do I manage memory consistency across multiple agents in a system? +
Memory consistency across multiple agents uses a shared episodic memory store with distributed locking for writes and read-through caching for reads. Each agent's memory writes go through a version controller that detects conflicts (two agents trying to update the same memory record simultaneously) and applies a merge strategy. The Glass-Box logging layer records all memory operations with agent identifiers, making memory consistency issues detectable and diagnosable.
What is the memory eviction policy and how do I configure it? +
Memory eviction policy determines when episodic memory entries are removed to stay within storage limits. The workshop covers three eviction strategies: TTL-based eviction (entries expire after a configured time period), LRU eviction (least recently accessed entries are evicted first), and importance-based eviction (entries below a importance threshold are evicted first, preserving high-importance memories regardless of age). The right strategy depends on your use case: TTL for session-specific contexts, importance-based for long-running agent relationships.
How do I test AI agent memory management for correctness? +
Testing AI agent memory management covers: unit tests for each memory operation (store, retrieve, compress, evict) with known inputs and expected outputs, integration tests that verify memory consistency across multiple agent invocations in a complete workflow, long-conversation tests that verify important context is retained through multiple compression cycles, and concurrent access tests that verify the locking mechanism prevents memory corruption under parallel agent load.
