What is an Agent Runtime?
An agent runtime is the execution environment that manages the lifecycle of an AI agent — handling the agent loop, tool execution, state management, concurrency, error recovery, and integration with external services via protocols like MCP.
WHY IT MATTERS
The agent runtime is to AI agents what Node.js is to JavaScript applications — it is the engine that actually runs the agent. While the LLM provides reasoning, the runtime handles everything else: executing tool calls, managing state, handling errors, and enforcing limits.
Runtime concerns include concurrency (how many agent loops run simultaneously), isolation (do agents share resources), persistence (is state saved across restarts), and observability (logging, tracing, metrics). These infrastructure-level decisions determine reliability and scalability.
The runtime is where tool calls originate. When the LLM decides to call a tool, the runtime executes that call — typically by sending an MCP request to a server. This is precisely where a policy-enforcing proxy can intercept and govern every tool call.
HOW POLICYLAYER USES THIS
PolicyLayer provides runtime policy enforcement at the MCP proxy level. Regardless of which runtime executes the agent — whether it is Claude Desktop, a custom Python script, or a framework like LangGraph — PolicyLayer governs tool calls at the protocol level. The runtime sends MCP requests through PolicyLayer, which evaluates them against YAML policies before forwarding to the server.