Agent Runtime Architecture

Agent runtime architecture refers to the technical execution environment in which AI agents process tasks, invoke tools, and manage state. It is the layer between the language model and external systems — defining how an agent plans steps, handles errors, coordinates parallel subtasks, and maintains context across sessions. Key components include the orchestrator (which controls execution flow), the tool registry (what capabilities the agent can call), session state (short-term working memory), and persistent workspaces (for long-running tasks that survive interruptions). Modern runtimes such as OpenAI Agents SDK v0.14, LangGraph, and Anthropic's native agent infrastructure differ primarily in how they handle state persistence, parallelism, and fault tolerance. Understanding runtime architecture is critical when agents need to do more than answer one-shot queries — especially for workflows that span hours, involve dozens of tool calls, and must recover gracefully from failures.