In this article
Problem: Fully autonomous agents can make irreversible mistakes — deleting data, sending emails, making payments — with no opportunity for correction.
Solution: Design agent workflows with explicit human checkpoints that pause execution, surface the intended action and its consequences, and require human approval before proceeding.
Implementation (LangGraph pattern):
- Define "interrupt" nodes in the graph for sensitive actions (e.g., "send_email", "delete_records")
- When the agent reaches an interrupt node, serialise the current state and pause execution
- Notify the human (Slack message, UI notification, email) with: proposed action, reasoning, expected consequences
- On approval: resume from the checkpoint. On rejection: route to replanning node
- Set time limits — auto-reject if no human response within N minutes
When to interrupt:
- Irreversible actions (send, delete, deploy, publish)
- High-cost operations (> $10 API call, > 1 hour compute)
- High-uncertainty steps (agent confidence < threshold)
Trade-Offs:
- ✔ Pro: Prevents catastrophic irreversible errors
- ✔ Pro: Builds user trust in agentic systems
- ✖ Con: Interrupt latency can be minutes to hours — not suitable for synchronous workflows
- ✖ Con: Human fatigue if too many interrupts are required (alert fatigue)
When To Use: Customer-facing agents with real-world consequences; regulated industries. When to avoid: Fully automated batch jobs with dry-run validation first.