Short answer: the OpenAI Agents SDK gives you the approval primitive — a tool call can pause for human review — but leaves the timeout, escalation, and fallback behavior entirely to your own application. Anter fails closed by default: an unattended or scheduled run auto-denies any tool needing approval instead of waiting indefinitely.
What "guardrails and human review" actually covers
OpenAI's Guardrails and Human Review docs describe the approval mechanism plainly: "your application approves or rejects the pending items." If review is going to take longer than the current process will run, the documented pattern is to "serialize state, store it, and resume later." That's a real, usable primitive — but it means timeout handling, escalation paths, and what happens if nobody ever resolves the approval are all your team's responsibility to build.
For an interactive chat session with a human in the loop, that's rarely a problem. For a scheduled or unattended run — a nightly batch job, a cron-triggered agent — it's a real gap: nothing in the SDK stops a run from sitting in a pending state forever if the approval is never actioned.
Anter's fail-closed default
Anter's permission model draws a hard line for unattended execution: any tool call requiring human approval in a run with no human present is auto-denied, not left pending. The run terminates cleanly with a specific termination reason instead of hanging. This is the default behavior, not a configuration a team has to discover and wire up after their first stuck run in production.
The underlying routing and delegation validation — write-time allowlist plus runtime existence check on every handoff — works the same way regardless of which LLM provider is powering a given agent, so this isn't an OpenAI-specific carve-out; it's how every unattended run behaves on the platform.
See the full side-by-side comparison for how Anter compares to Claude Managed Agents, CrewAI, and Base44 on the same dimensions.