Agent memory and context engineering

Understand the parts of an agent context stack, then choose an integration and evaluate it on your own tasks.

• What is a context database for AI agents?

  A context database stores and retrieves the information an AI agent needs to work: reference material, persistent memory, and reusable skills. OpenViking is an open-source implementation that exposes this context as a navigable virtual filesystem with viking:// addresses.

• Agent memory: persistent context beyond one conversation

  Agent memory is information retained beyond the model's current context window and retrieved when a later task needs it. It can include user preferences, decisions, task events, and reusable experience. A larger context window alone does not provide this persistence.

• Context engineering for agents: retrieval, memory, and assembly

  Context engineering is the work of selecting, organizing, and delivering the information an AI model needs at each step. For agents, it includes instructions, retrieved evidence, tool results, conversation history, memory, and the rules for fitting them into a limited context window.

• What is an agent harness, and where does memory fit?

  An agent harness is the runtime around a model that manages its task loop, tools, execution environment, and conversation lifecycle. A memory or context backend supplies retained information to that runtime. OpenViking can serve as this backend for an existing harness.