Agent Runtime
Definition
The execution environment that hosts an autonomous agent — managing the agent's event loop, tool execution, memory access, state persistence, and lifecycle across sessions. The runtime is the infrastructure layer between the model and the tools: it receives the model's tool call specifications, executes them, returns results, and maintains the conversation context across turns. Runtimes vary significantly in what they provide: some are minimal wrappers (a Python script with a while loop), others are full-featured systems with built-in persistence, retry logic, observability, horizontal scaling, and multi-agent coordination. Runtime choice determines reliability, debuggability, and operational cost more than model choice.
Builder Context
Most early-stage agent failures are runtime failures, not model failures. The agent gives up, loops, or takes wrong actions because the runtime doesn't handle tool failures gracefully, doesn't persist state correctly, or runs out of context. Before optimizing your prompts, audit your runtime: does it retry transient failures? Does it surface tool call errors to the model in a format it can reason about? Does it truncate context correctly when approaching limits? For production: LangGraph is the most battle-tested graph-based runtime. For simple linear agents: a clean ReAct loop in Python is more reliable than a complex framework. For long-running async agents: Temporal provides durable execution guarantees that no agent framework currently matches.