AI agents aren’t just chatting anymore; they're editing files, running shell commands, and deploying code. That shift turns a familiar Python convenience into a reliability trap: async concurrency. A few innocent asyncio.gather(...) calls later, your agent is running tests before the config file exists, reading output from a tool that hasn’t finished, and “fixing” errors that only happened because the timeline got shuffled. The crash is annoying. The real damage is worse: the model is forced to reason over a non-linear history, and that’s where the hallucinated “I already did that” loops come from. This talk introduces the Lane Queue pattern (inspired by the architecture used in OpenClaw): a simple orchestration layer built around a “Default Serial, Explicit Parallel” rule. You’ll leave with a production-ready approach in Python: implementing a lane with asyncio.Queue + asyncio.Lock, adding queue draining to stop failure cascades, carving out side lanes for read-only parallel work, and using serialization to make human approval gates for high-risk commands practical.