The "Human-in-the-Loop" Playbook: Standard Operating Procedures for AI Reviewers
Why "Set and Forget" AI Fails
At CLOUDTECH IT, our agentic AI deployments follow a different philosophy. We treat human review not as a bottleneck, but as a quality multiplier. When a human reviewer catches an error, that intervention does more than fix a single output. It generates training signal. It creates documentation. It builds the institutional knowledge that makes the system smarter tomorrow than it was yesterday.
The Intervention Workflow: When Humans Step In
Tier 1 — Automatic Approval
85% Confidence
Output proceeds directly to delivery
Tier 2 — Human Review Required
60% and 84% Confidence
Output is routed to a reviewer queue
Tier 3 — Escalation
<60% Confidence
Output presented only as a draft suggestion
Not every AI output requires human eyes. The operational question is: which ones do? Our systems use a confidence-based routing model with three tiers.
Tier 1 — Automatic Approval: When an AI agent completes a task and returns a confidence score of 85% or higher, the output proceeds directly to delivery. These are the routine cases where the model has high certainty and historical accuracy supports that confidence. Examples include standard email categorisation, document formatting corrections, and FAQ responses that match established templates exactly.
Tier 2 — Human Review Required: When confidence falls between 60% and 84%, the output is routed to a reviewer queue. The agent has produced a response, but uncertainty exists. Perhaps the query contained ambiguous phrasing, or the response required synthesis across multiple data sources. A human reviewer examines the output, approves it unchanged, modifies it, or rejects it entirely.
Tier 3 — Escalation: Confidence below 60% triggers immediate human handling with the AI output presented only as a draft suggestion. The human becomes the primary author, with the agent’s work serving as a starting point rather than a near-finished product. Additionally, certain trigger conditions bypass confidence scores entirely: any output mentioning legal liability, financial figures above defined thresholds, or content flagged by safety classifiers goes directly to human review regardless of confidence.
The Five-Point Review Checklist
1. Accuracy
comes first. Is the factual content correct? Does the output contain hallucinated information, outdated data, or logical contradictions? Reviewers verify any specific claims against source material and flag outputs that present assumptions as facts.
2. Tone
follows. Does the voice match the intended audience and context? An AI might produce technically accurate content that reads as inappropriately casual for a formal proposal, or overly stiff for a customer service interaction. Tone mismatches erode trust even when information is correct.
3. Safety
requires vigilance. Could this output cause harm if misunderstood or misused? Does it inadvertently provide instructions for dangerous activities, express biased perspectives, or make promises the organisation cannot keep? Safety review is particularly critical for customer-facing content.
4. Formatting
ensures usability. Is the output structured correctly for its destination? Proper heading hierarchy, consistent list formatting, appropriate paragraph length, and correct use of emphasis all fall under this criterion. A well-reasoned response loses value if it arrives as an unreadable wall of text.
5. PII Protection
guards privacy. Does the output inadvertently expose personally identifiable information? Has the AI included email addresses, phone numbers, internal reference codes, or other sensitive data that should have been redacted? This check applies to both input reflection and generated content.
The Five-Point Review Checklist
"Every human correction is a training example waiting to happen."
When a reviewer modifies an AI output, that modification creates value beyond the immediate fix. Our systems capture three data points from every intervention: the original AI output, the corrected version, and a category tag describing the error type.
These tagged corrections accumulate over time. A pattern of “tone too formal” tags on customer service responses indicates a fine-tuning opportunity. Repeated “factual error” tags on a specific topic reveal a knowledge gap requiring retrieval augmentation. The feedback loop transforms reactive fixes into proactive improvements.
The mechanics matter. Corrections are stored in a structured format that preserves context: the original prompt, the initial output, the human revision, the error category, and a severity rating. Quarterly reviews examine accumulated corrections to identify systemic patterns. High-frequency error categories become fine-tuning priorities. The result is a system that genuinely learns from its mistakes rather than repeating them indefinitely.
The Reviewer Cheat Sheet: Your Reference Artifact
Alert
Theory is useful. Reference materials are essential. Below is the single-page Reviewer Cheat Sheet we provide to all staff conducting human-in-the-loop review. It condenses the workflow triggers, the five-point checklist, and the feedback tagging categories into a quick-reference format suitable for printing or screen display.
Download the Reviewer Cheat Sheet to implement these SOPs in your own AI oversight processes. Adapt the confidence thresholds to your risk tolerance. Modify the checklist weightings to your context. The framework scales from small teams reviewing a handful of outputs daily to enterprise operations processing thousands.
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