Models

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Model outputs are the responses AI agents give you. Quality control here determines whether automation becomes your competitive advantage or your liability.

What

Model Output = Agent’s Response to Your Instructions

When you give an agent a task, it produces an “output” - the completed work, answer, or analysis you requested.

Examples:

Input: “Categorize this customer email”
Output: “Category: Billing Question, Priority: Medium”
Input: “Extract key info from this invoice”
Output: “Vendor: ABC Corp, Amount: $2,500, Due: March 15”
Input: “Write a professional response to this complaint”
Output: [Generated email response]

Why this matters: Agent outputs directly impact your customers, processes, and business decisions. Poor quality erodes trust. High quality builds competitive advantage through reliability at scale.

Purpose

Quality control prevents costly mistakes:

Business Impact

Risk: Wrong responses sent to customers Protection: Review outputs before they go live Result: Maintain professional reputation while scaling faster than competitors

Data Accuracy

Risk: Incorrect data entered into systems Protection: Validate extracted information Result: Clean data enables better decisions at enterprise speed

Process Reliability

Risk: Automated decisions based on bad info Protection: Monitor agent performance patterns Result: Scale operations without proportional cost increases

Types

Agents produce different kinds of outputs:

Extracted Data:

Customer: John Smith
Email: john@company.com
Request: Billing question
Priority: High

Generated Text:

Dear Mr. Smith,
Thank you for contacting us about your billing question.
I've reviewed your account and will have an answer within 24 hours.
Best regards, Customer Service

Classifications/Categories:

Document Type: Invoice
Department: Finance
Action Required: Yes
Confidence: 85%

Analysis/Summaries:

Contract Summary: 2-year service agreement with ABC Corp
Key Terms: $50K annual value, quarterly payments
Risk Level: Low

Quality

Use these 30-second checks before trusting agent output with business operations.

Checklist

Before deploying any agent:

Test with 10 real examples:

✅ Agent handles typical cases correctly
✅ Agent flags unusual cases for review
✅ Output format matches your needs
✅ No sensitive data leaks in responses

Edge case testing:

✅ Blank inputs → “NEEDS REVIEW”
✅ Unclear requests → escalation message
✅ Out-of-scope questions → polite redirect

Monitoring

Spot Check

Every day: Review 3 random agent outputs Red flag: Same mistake appearing multiple times

Error Rate

Track: % of outputs needing human correction Target: Under 5% for routine tasks

Escalation Rate

Monitor: % of cases flagged for review Sweet spot: 10-20% (catches edge cases without over-flagging)

Fixes

Agent misses details: Add to template: “Always extract: [specific field]”

Agent too cautious: Reduce confidence threshold: “Flag only if confidence under 70%”

Agent not cautious enough: Add safety net: “If any doubt, mark NEEDS REVIEW”

Wrong tone/format: Add examples: “Write like: [show exact example]“

Confidence

Good agents tell you how certain they are:

90%+ → Usually safe to trust
70-89% → Quick human review
Under 70% → Human handles

Add to all templates: “Include confidence score 1-100”

Intervene

Stop and retrain if:

Error rate jumps above 10%
Same mistake happens 3+ times
Customers complain about agent responses
Agent starts handling cases outside its scope

Monthly review:

Which task types work best?
Which need human backup?
Where can we expand agent use?

Explore related concepts:

Tokens - Understand how agents measure and cost work
Security - Learn safe deployment practices
Data - Prepare your data for agents

Quality outputs require quality inputs.