🖥️ AI InfrastructureID:
agent-runtimeAgent Runtime (cross-module)
Agent 运行时(跨模块)
🎯Core Definition
the agent runtime is the engineering backbone of the LLM agent loop — tool-execution sandbox (isolation/permissions/timeout), task queue and scheduling, and retry/idempotency control.
💡Use Cases
agent systems that actually execute code, call APIs, or drive browsers; sandbox security, timeout reclamation, and retry strategies are frequent interview questions.
⚡Key Problems Solved
unsandboxed tool execution risks command injection, resource exhaustion, and hung-task leaks; containerized, permission-restricted sandboxes plus strict timeouts and idempotent retries bound both security risk and failure rates. See the AI_Engineering module: orchestration and scheduling details in the AI_Engineering module (guide agent-design-patterns).
🎯5 High-Frequency Exam Points
1
How do sandboxes isolate tool execution (permissions, timeouts) against malicious or runaway calls?
2
How do task queues and scheduling support high-concurrency agent requests (priority, concurrency caps, backpressure)?
3
Retry strategies and idempotency for failed tool calls (at-least-once vs exactly-once)?
4
How to cap resource usage (CPU/memory/network/filesystem) for sandboxed code execution?
5
How are timed-out or hung tool calls reclaimed to avoid task leaks?
📖 In-depth Guide:📄 agent-design-patterns →
Updated 2026-08-12🎯
🚀 Start Card Practice ➔Test Your Knowledge: Practice Questions for "Agent Runtime (cross-module)"
Single choice pitfall questions with instant feedback and mistake tracking.