Reviewer Agent

AI Agents Learning Path – Article 07

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Introduction

Planning is important.

Execution is important.

But one important question remains...

How do we know the AI generated the correct answer?

Imagine an AI Agent generates:

Transfer $50,000

Should the system immediately execute it?

Absolutely not.

Every enterprise AI system needs a validation layer before returning the final response or executing critical business actions.

This responsibility belongs to the Reviewer Agent.

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What is a Reviewer Agent?

A Reviewer Agent validates the output generated by another AI Agent before it reaches the user.

It checks whether the response is:

• Correct
• Complete
• Safe
• Policy compliant
• Supported by available data
• Free from hallucinations

The Reviewer Agent acts as a Quality Assurance (QA) layer for AI systems.

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Real-World Analogy

Think about software development.

Developer

↓

Code Review

↓

Merge

↓

Production

The developer writes the code.

Another engineer reviews it.

Similarly,

Planner Agent

↓

Executor Agent

↓

Reviewer Agent

↓

User

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High-Level Architecture

flowchart LR

User[User]

Planner[Planner Agent]

Executor[Executor Agent]

Reviewer[Reviewer Agent]

LLM

Response

User --> Planner
Planner --> Executor
Executor --> Reviewer
Reviewer --> LLM
LLM --> Response
Response --> User

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Responsibilities

The Reviewer Agent validates:

Validation	Purpose
Completeness	Is every task finished?
Accuracy	Is the answer correct?
Hallucination Detection	Did the AI invent information?
Policy Compliance	Does it follow business rules?
Security	Is sensitive data exposed?
Formatting	Is the output well structured?

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Review Workflow

flowchart TD
    TASK["Task Completed"]
    RECEIVE["Receive Result"]
    VALIDATE["Validate Output"]
    VERIFY["Verify Facts"]
    POLICY["Check Policies"]
    APPROVED{"Approved?"}
    RESPONSE["Return Response"]
    REJECT["Reject"]
    REEXEC["Request Re-execution"]

    TASK --> RECEIVE
    RECEIVE --> VALIDATE
    VALIDATE --> VERIFY
    VERIFY --> POLICY
    POLICY --> APPROVED

    APPROVED -->|Yes| RESPONSE
    APPROVED -->|No| REJECT

    REJECT --> REEXEC

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Example

User asks:

Summarize this financial report.

Executor generates:

Revenue

↓

Expenses

↓

Profit

↓

Summary

Reviewer checks:

• Are calculations correct?
• Are all sections included?
• Is confidential data exposed?
• Does the summary match the report?

Only then is the response returned.

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Review Lifecycle

sequenceDiagram

participant User
participant Planner
participant Executor
participant Reviewer

User->>Planner: Business Request

Planner->>Executor: Execute Tasks

Executor-->>Reviewer: Generated Result

Reviewer->>Reviewer: Validate

alt Valid
Reviewer-->>User: Final Response
else Invalid
Reviewer-->>Executor: Re-execute Task
end

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Validation Checklist

A Reviewer Agent typically verifies:

Response Generated

↓

Business Rules

↓

Tool Output

↓

Security Rules

↓

Formatting

↓

Final Approval

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Banking Example

Customer asks:

Transfer $10,000

Reviewer validates:

Customer Authenticated?

↓

Daily Limit Exceeded?

↓

Fraud Check Passed?

↓

Account Balance Available?

↓

Approve Transfer

If any validation fails, execution stops.

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HR Example

Employee asks:

Apply leave next Monday.

Reviewer checks:

Leave Balance

↓

Holiday Calendar

↓

Manager Approval Required?

↓

Policy Compliance

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Insurance Example

Customer asks:

What's my claim amount?

Reviewer validates:

Claim Exists?

↓

Payment Approved?

↓

Correct Currency?

↓

Correct Customer?

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Healthcare Example

Doctor requests:

Generate patient summary.

Reviewer verifies:

Correct Patient

↓

Complete Medical History

↓

No Missing Reports

↓

No Sensitive Data Leakage

Important: AI-generated healthcare summaries should always be reviewed by qualified healthcare professionals before clinical use.

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Hallucination Detection

One of the Reviewer Agent's most important responsibilities is identifying hallucinations.

Example:

Question:

What is my account balance?

Tool returns:

$4,500

LLM answers:

Your balance is $45,000.

Reviewer detects:

Mismatch

↓

Reject Response

↓

Regenerate

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Enterprise Architecture

flowchart TD
    USERS["Users"]
    GATEWAY["API Gateway"]
    APP["Spring Boot"]

    PLANNER["Planner"]
    EXECUTOR["Executor"]
    REVIEWER["Reviewer"]

    MEMORY["Memory"]
    API["Business APIs"]
    LLM["LLM"]

    USERS --> GATEWAY
    GATEWAY --> APP

    APP --> PLANNER
    PLANNER --> EXECUTOR
    EXECUTOR --> REVIEWER

    REVIEWER --> MEMORY
    REVIEWER --> API
    REVIEWER --> LLM

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Reviewer Decision Flow

flowchart TD
    RESPONSE["Response"]
    BUSINESS["Business Validation"]
    SECURITY["Security Validation"]
    POLICY["Policy Validation"]
    PASS{"Pass?"}
    RETURN["Return"]
    REPLAN["Replan"]

    RESPONSE --> BUSINESS
    BUSINESS --> SECURITY
    SECURITY --> POLICY
    POLICY --> PASS

    PASS -->|Yes| RETURN
    PASS -->|No| REPLAN

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Reviewer vs Planner

Planner	Reviewer
Creates plan	Validates result
Works before execution	Works after execution
Breaks tasks	Reviews completed work
Chooses tools	Validates tool output

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Reviewer vs Executor

Executor	Reviewer
Performs work	Reviews work
Calls APIs	Validates API responses
Executes workflow	Verifies workflow results
Returns output	Approves output

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Common Validation Rules

A Reviewer Agent should validate:

• Required fields present
• JSON schema
• SQL syntax
• API response
• Business rules
• Security policies
• Data consistency
• Sensitive information
• Response quality
• Output format

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Best Practices

✅ Review every critical business action.

✅ Validate tool responses.

✅ Detect hallucinations.

✅ Verify structured outputs.

✅ Apply business rules.

✅ Log review decisions.

✅ Re-execute failed tasks when appropriate.

✅ Keep review logic independent of execution.

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Common Mistakes

❌ Returning AI output without validation.

❌ Trusting every LLM response.

❌ Ignoring business policies.

❌ Skipping security checks.

❌ No hallucination detection.

❌ No audit trail.

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Enterprise Use Cases

Reviewer Agents are essential for:

• Banking Transactions
• Insurance Claims
• Healthcare Systems
• HR Platforms
• Financial Reports
• Enterprise Search
• AI Code Generation
• SQL Generation
• Contract Review
• Compliance Automation

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Advantages

✅ Higher accuracy

✅ Reduced hallucinations

✅ Better compliance

✅ Improved security

✅ Higher customer trust

✅ Enterprise-ready quality control

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Challenges

• Additional processing time
• Complex validation rules
• Dynamic business policies
• Cost of multiple AI calls
• False positives during validation

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Enterprise AI Agent Pipeline

flowchart LR

Goal

Planner

Executor

Reviewer

Memory

User

Goal --> Planner
Planner --> Executor
Executor --> Reviewer
Reviewer --> Memory
Reviewer --> User

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Summary

In this article, you learned:

• What a Reviewer Agent is
• Why validation is essential
• Hallucination detection
• Business rule validation
• Security verification
• Enterprise architecture
• Banking, HR, Insurance, and Healthcare examples
• Best practices

The Reviewer Agent is the quality assurance layer of an enterprise AI system. It ensures that AI-generated outputs are accurate, secure, compliant, and trustworthy before they reach users or trigger business actions. By separating planning, execution, and review, organizations can build AI systems that are far more reliable and suitable for production use.

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