The Memory Problem

Every agent faces the same fundamental challenge: context windows are finite, but useful memory should be infinite. How do you build systems that let agents remember what matters without drowning in noise?

Types of Agent Memory

We use a three-tier memory architecture:

1. Working Memory

Short-term context for the current task. This is what fits in the context window—recent messages, current state, immediate goals.

Working Memory
├── Current task context
├── Recent conversation history
├── Active tool states
└── Immediate next steps

2. Episodic Memory

Searchable logs of past interactions and decisions. When an agent needs to remember "what did I do last time this happened?", it queries episodic memory.

3. Semantic Memory

Long-term knowledge distilled from experience. Facts, patterns, and learned behaviors that persist across sessions.

The Retrieval Challenge

Memory is only useful if you can find what you need. We use a hybrid approach:

• Keyword search for exact matches
• Semantic search for conceptual similarity
• Temporal filtering for recent relevance
• Importance scoring to surface critical memories

Memory Maintenance

Memories decay. We run periodic processes to:

1. Consolidate similar memories
2. Prune outdated information
3. Extract patterns from episodic to semantic memory
4. Update importance scores based on access patterns

Practical Implementation

Here's a simplified version of our memory interface:

interface AgentMemory {
  // Working memory
  getContext(): Context;
  updateContext(update: ContextUpdate): void;

  // Episodic memory
  remember(episode: Episode): void;
  recall(query: Query): Episode[];

  // Semantic memory
  learn(fact: Fact): void;
  know(topic: string): Fact[];
}

The key insight is that these three systems work together. Working memory knows what's happening now. Episodic memory knows what happened before. Semantic memory knows what those experiences mean.

What's Next

Memory is just one piece of the agent architecture puzzle. In future posts, we'll cover:

• Decision frameworks for autonomous action
• Tool integration patterns
• Multi-agent coordination

Building memory systems that scale is hard. But it's also the foundation for agents that actually get better over time.

Want the full picture? Check out Chapter 2 of The Agent Operator's Manual for our complete guide to shared state and memory systems.