🤖 Ghostwritten by Claude · Curated by Tom Hundley

This article was written by Claude and curated for publication by Tom Hundley.

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The Reviewer Pattern: Automated QA for Agent Code

Trust, but verify. Then verify again.

The Fear of Autonomy

The single biggest blocker to AI adoption in the enterprise is the fear of the runaway agent.

• What if the agent deletes the wrong database table?
• What if the agent emails a customer with a hallucination?
• What if the agent writes security vulnerabilities into the code?

These are valid fears. If you rely on a single agent to do the work, you are gambling.

The Two-Agent Architecture

At Elegant Software Solutions, we rarely deploy a single agent for a complex task. We deploy a Team.

The most common pattern we use is the Builder/Reviewer Pattern.

1. The Builder (The Junior)

• Role: Do the work. Write the code. Draft the email.
• Persona: Creative, fast, eager.
• Prompt: You are a Python expert. Write a script to analyze this CSV.

2. The Reviewer (The Senior)

• Role: Critique the work. Find bugs. Check against policy.
• Persona: Pedantic, cautious, strict.
• Prompt: You are a Senior Security Engineer. Review the following code for SQL injection risks and logic errors. If you find ANY, reject the code and explain why.

How It Works in Practice

1. Builder generates a solution.
2. System passes that solution to the Reviewer.
3. Reviewer analyzes it.
   • Scenario A: Looks good. Approved. - Action Executed.
   • Scenario B: Reject. You used a hardcoded password. - Feedback Loop.
4. Builder receives the rejection, reads the feedback, and tries again.
5. Loop continues until approval or max retries.

Why This Changes the Game

This pattern solves three massive problems:

1. Safety: The Reviewer can be given a completely different Constitution or set of rules focused purely on safety, separate from the Builders goal of completion.
2. Quality: We catch 80% of hallucinations because it is statistically unlikely that both models will hallucinate the exact same wrong fact in the exact same way.
3. Human Trust: When we show a client the logs—Look, the AI caught its own mistake and fixed it—their confidence in the system skyrockets.

Implementation Details

We implement this using LangGraph, where the Review node acts as a conditional edge.

def reviewer_node(state):
    feedback = llm.invoke([system_prompt, state[code]])
    if APPROVED in feedback:
        return execute
    else:
        return revise

Conclusion

You wouldnt let a junior human engineer push code to production without a code review. Dont let your AI agents do it either.

The Reviewer Pattern is the difference between a toy and a teammate.

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*This concludes Part 3. In the final part, well discuss [Unit Testing for Stochastic Systems](/blog/unit-testing-llm-agents-pytest).*

How This Article Was Made

This article is a live example of the AI-enabled content workflow we build for clients.

Stage	Who	What
Research	Claude Opus 4.5	Analyzed current industry data, studies, and expert sources
Curation	Tom Hundley	Directed focus, validated relevance, ensured strategic alignment
Drafting	Claude Opus 4.5	Synthesized research into structured narrative
Fact-Check	Human + AI	All statistics linked to original sources below
Editorial	Tom Hundley	Final review for accuracy, tone, and value

The result: Research-backed content in a fraction of the time, with full transparency and human accountability.

Why We Work This Way

Were an AI enablement company. It would be strange if we didnt use AI to create content. But more importantly, we believe the future of professional content isnt AI vs. Human—its AI amplifying human expertise.

Every article we publish demonstrates the same workflow we help clients implement: AI handles the heavy lifting of research and drafting, humans provide direction, judgment, and accountability.

Want to build this capability for your team? Lets talk about AI enablement →

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