State & Memory Across Steps
Working memory, structured state, context window management, and persistent memory.
Context Window Pressure
Every agent step consumes context window space, and when the window fills up, the agent must either summarize, prune, or fail — making token budgeting a core engineering concern for long-running agents.
Conversation as Working Memory
The message history in an agent loop functions as working memory, accumulating context that shapes every subsequent reasoning step and tool invocation.
Persistent Memory Across Sessions
Working memory vanishes when an agent session ends; persistent memory uses checkpointing, databases, and long-term stores to let agents remember information across separate invocations.
Structured State Management
When conversation history alone cannot reliably track complex agent state, typed state objects and explicit key-value stores give agents a structured, programmatically accessible memory that survives context window pressure.