Help & FAQ
Everything you need to know about human-in-the-loop approvals and TofuLoop.
Concepts
- Human-in-the-loop vs full automation: what's the difference? HITL inserts human checkpoints for risky decisions; full automation runs end-to-end without review.
- What is an approval workflow for AI agents? A workflow where an agent proposes an action and waits for a human approve/reject decision before executing.
- Human-in-the-loop vs manual review: what's the difference? Manual review is all-human; HITL lets automation handle the bulk and escalates only the cases that need judgment.
- What is human-in-the-loop (HITL)? A design pattern where automated systems pause at critical decision points to get human approval before proceeding.
- Why are AI hallucinations dangerous in production systems? Because confident, wrong outputs can trigger real actions—causing financial, legal, or reputational harm.
- Why do AI agents need human oversight? Because agents can act confidently on incomplete or wrong context; oversight reduces risk and adds accountability.
Implementation
- What are approval gates for AI agents? Approval gates are checkpoints where an agent must receive a human decision before executing a risky tool/action.
- Event-driven approval workflows for AI Approvals triggered by events (amount thresholds, tool usage, policy matches) rather than constant manual review.
- What are escalation rules for AI agents? Rules that force an agent to pause and ask for help when risk is high, confidence is low, or retries fail.
- How do approval workflows work? They route a proposed action to an approver, collect a decision, then proceed (or stop) with an auditable record.
- How to implement human-in-the-loop systems Pick intervention points, present context to reviewers, record decisions, and feed outcomes back into your system.
Best Practices
- What are confidence thresholds in AI systems? A confidence threshold is a cutoff that decides whether AI proceeds automatically or escalates for review.
- How to design approval workflows for AI Define the risk triggers, approvers, required context, timeouts, and audit trail—then iterate based on outcomes.
- How to prevent AI from making expensive mistakes Combine approvals, limits, monitoring, and rollback so failures are caught early and blast radius stays small.
- How to reduce human approvals over time Use metrics and confidence thresholds to remove approvals where the system is consistently correct, while keeping safeguards.
- When should AI systems act autonomously? When actions are low-risk, reversible, and the system has proven accuracy with monitoring and rollback.
Governance & Compliance
- Why AI decisions need audit logs Audit logs capture inputs, outputs, versions, and approvals so decisions are traceable, reviewable, and defensible.
- Why compliance often requires human-in-the-loop Regulated decisions need explainability and accountability; human review provides oversight and defensible process.
- What actions should require human approval in AI workflows? High-impact, irreversible, customer-facing, or compliance-sensitive actions should be gated by approval.
- Who is responsible for AI agent actions? The deploying organization remains responsible; tools don’t carry accountability—people and processes do.
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