TECHNIQUE
Agentic Orchestration
Human-in-the-loop checkpoints are implemented as explicit gates: platform approval before AI use, reviewer validation before publication/escalation, or developer review before code-change acceptance.
Require a platform-team review checkpoint before a new AI use case is onboarded: Grab requires “a mini-RFC” and “a checklist” for every new use case, reviewed by the platform team.
1 of 3 operators with cited human-checkpoint evidence in this pool.Validate generated incident documentation before publishing it: Agoda has a reviewer validate the LLM-drafted content and publish the final version to the internal documentation system.
1 of 3 operators with cited human-checkpoint evidence in this pool.Escalate to human review when the LLM lacks enough context: Agoda says that when the LLM does not have full context, it is better to over-escalate and route the case for human review.
1 of 3 operators with cited human-checkpoint evidence in this pool.Use human analyst peer review to validate model conclusions: Agoda reports 97%+ alignment with human analyst conclusions during peer review over the last three months.
1 of 3 operators with cited human-checkpoint evidence in this pool.Send AI-generated optimization insights to downstream tooling or developer manual review rather than treating the model output alone as the final action.
1 of 3 operators with cited human-checkpoint evidence in this pool.Across the evidence-backed deployments, the human checkpoint is a named gate at a workflow boundary: platform-team approval before AI use, reviewer validation or escalation in incident-response workflows, or developer manual review for code-optimization outputs.
Operators place the human checkpoint at different stages of the AI workflow.
APPROACH 01
Upfront governance gate before a new AI use case is allowed onto the platform.
APPROACH 02
Post-generation review gate before incident documentation is published, plus escalation to human review when context is incomplete.
APPROACH 03
Developer acceptance/review gate after AI-generated optimization insights are produced and passed downstream.
Operators explicitly keep review paths because model outputs can be wrong or under-contexted: Agoda routes low-context cases to human review, while Uber adds validation to catch false positives and reduce hallucinations before suggestions reach downstream workflows.
| Name | Kind | When | Maturity |
|---|---|---|---|
| Approval queue with resumable state | pattern | high-stakes actions pause for human sign-off and resume exactly where they stopped | established |
| LangGraph interrupts | library | graph-native pause/resume already running on LangGraph | established |
| Temporal signals | service | human input arrives asynchronously into long-lived durable workflows | established |