Building reliable AI agents is less about the model and more about state, memory, and consistency. State tracks the current workflow (what's done, what's next, active constraints), while memory spans across tasks in three forms: short-term (current task context), long-term (user preferences and patterns), and external (live data from APIs and databases). The key design principle is 'react fast with state, learn slowly with memory' — state updates immediately on new instructions, but long-term memory only updates after a change proves stable. A four-layer reference architecture (brain, state, memory, external systems) keeps responsibilities clean. The post also covers context window budgeting, memory lifecycle management (create/update/summarize/delete), common memory failure modes (stale data, wrong captures, retrieval failures), and a practical checklist for building stateful agents that scale without degrading.
Table of contents
Tracking what the agent is doing with the stateCarrying information across tasks with memoryStaying consistent when preferences changeBuild travel agent from three piecesReference architecture for a stateful agentDeciding what fits in the context windowHow memory goes wrongRemember the right things, NOT everythingReferencesSort: