Working Memory in AI Agents - Context & Short-term Storage

What is Working Memory?

Working memory is the cognitive system that temporarily holds and manipulates information needed for the current task. In AI agents, working memory typically corresponds to the context window - the information available during a single interaction or reasoning step.

The Context Window

For LLM-based agents, working memory is constrained by:

Token limits (4K, 8K, 32K, 128K+ tokens)

Attention mechanisms

Computational costs

Latency requirements

Working Memory Contents

An agent's working memory typically includes:

Current conversation messages

System prompts and instructions

Retrieved context from long-term memory

Tool outputs and intermediate results

Scratchpad for reasoning

Managing Limited Capacity

Strategies for working with limited context windows:

**Summarization**: Compress older context

**Sliding Window**: Drop oldest messages

**Selective Retrieval**: Only include relevant information

**Hierarchical Context**: Summaries + details on demand

Working vs Long-term Memory

| Aspect | Working Memory | Long-term Memory |

|--------|----------------|------------------|

| Duration | Current session | Persistent |

| Capacity | Limited (tokens) | Unlimited |

| Access | Immediate | Requires retrieval |

| Update | Automatic | Explicit storage |

Implementation Patterns

Common working memory patterns:

**Buffer Memory**: Recent N messages

**Summary Memory**: Compressed conversation history

**Entity Memory**: Tracked entities from conversation

**Knowledge Graph Memory**: Structured current context

Optimizing Working Memory

Best practices for working memory management:

Prioritize recent and relevant information

Use structured formats to reduce tokens

Implement smart truncation strategies

Balance detail vs coverage

Monitor token usage