Agent Runtime
The managed execution substrate where agents actually run -- especially unattended. The platform counterpart to Loop Engineering: triggers that start work without a keystroke, a scheduler and queue, workers that run each agent in a sandbox, concurrency control, and lifecycle management. A CI/CD runner farm, but for agents.
The Pattern
"Codex runs coding tasks in cloud sandboxes, in parallel, off in the background, and comes back with pull requests." -- on OpenAI Codex, via Jazz Tong (source)
An agent runtime is the managed execution substrate where agents actually run -- especially unattended. It is the platform counterpart to Loop Engineering: the practice designs a loop; the runtime runs loops for everyone. It provides the triggers that start work without a keystroke (schedules, webhooks, CI events), a scheduler and queue that dispatch jobs to workers, the compute that executes each run -- each one dropped into a sandbox -- concurrency and fan-out control, and lifecycle management: retry, resume, kill, and state checkpointing so a long run survives a restart.
Think CI/CD runner farm, but for agents. Codex cloud tasks, Cursor background agents, and open frameworks like LangChain's Open SWE are all instances (LangChain).
Why It Matters
"Run unattended loops" has no home without a runtime -- something has to decide what runs, when, where, and how many, and what to do when a run fails. Caps matter for cost as much as safety: production systems bound fan-out (for example, 16 concurrent and 1,000 total agents per run) (Jazz Tong). The runtime is where the AI SDLC executes -- the substrate that turns "let it run overnight" into something operable.
Sources
Last reviewed: 2026-06-24