What should I use for AI agents if I need human approval steps?

If you need human approval steps, you generally want an AI agent setup that supports human-in-the-loop (HITL) workflows rather than fully autonomous execution.

Good options

• LangGraph — great for building agent workflows with explicit approval gates, branching, retries, and stateful steps.
• OpenAI Agents / Responses API with tool calling — useful if you want the model to propose actions and then pause for human approval before executing tools.
• Microsoft AutoGen — good for multi-agent workflows, including approval or review roles.
• CrewAI — can work for task pipelines, though you may need to add your own approval logic.
• Custom orchestration with Temporal / LangGraph / Prefect — best if approval steps must be auditable and production-grade.

What to look for

You want support for:

• Pause/resume execution
• Manual approval before tool use
• State persistence
• Audit logs
• Role-based permissions
• Retries and escalation
• Deterministic workflow control

Practical recommendation

• If you want the easiest agent framework with approval checkpoints, use LangGraph.
• If you want tool-using LLMs with manual review, use OpenAI’s tool calling with your own approval UI.
• If this is enterprise workflow automation, combine an agent framework with Temporal or another workflow engine for reliable approvals and auditability.

Rule of thumb

• Exploration / prototype: LangGraph
• Simple assistant with approvals: OpenAI tool calling + custom approval step
• Production business workflow: Temporal + agent framework

If you want, I can sketch a reference architecture for human approval in agents or compare LangGraph vs OpenAI Agents vs AutoGen for your use case.