AI-Powered Code Review Assistant: Part 2 - Production Considerations

In Part 1, we built the core architecture for an AI code review assistant: diff parsing, four specialized review agents, and a finding aggregator. Now for the hard questions.

How much does this actually cost per pull request? How do you debug why the security agent missed a real vulnerability? Should you use Python or C# for this? And when should you skip AI review entirely and stick with human-only workflows?

This article covers the production realities that separate a working demo from a system your team actually trusts.

Cost Analysis: What This Really Costs Per Review

Token costs are the primary variable expense. Each review runs 4 agent calls (one per agent), each processing the same diff chunks with different prompts. Here's the breakdown for a typical PR:

These numbers are for Azure OpenAI GPT-4o at $2.50/1M input tokens and $10.00/1M output tokens (pay-as-you-go pricing as of early 2026).

Monthly Cost Projection

Infrastructure costs include Azure Functions (or App Service) for the review service, plus storage for review history. The token costs dominate only at very high volume.

Observability: Tracing Agent Decisions

When the security agent misses a real vulnerability, or the style agent flags valid code as a violation, you need to understand why. This requires structured logging at every stage of the pipeline.

import structlog
from uuid import uuid4

logger = structlog.get_logger()

class ReviewTracer:
    def __init__(self, pr_id: str):
        self.review_id = str(uuid4())
        self.pr_id = pr_id
        self.log = logger.bind(
            review_id=self.review_id,
            pr_id=pr_id,
        )

    def trace_agent_call(
        self,
        agent: str,
        file_path: str,
        input_tokens: int,
        output_tokens: int,
        findings_count: int,
        duration_ms: float,
    ):
        self.log.info(
            "agent_review_complete",
            agent=agent,
            file_path=file_path,
            input_tokens=input_tokens,
            output_tokens=output_tokens,
            findings_count=findings_count,
            duration_ms=duration_ms,
            cost_usd=self._calculate_cost(
                input_tokens, output_tokens
            ),
        )

    def trace_aggregation(
        self,
        total_findings: int,
        deduplicated: int,
        final_count: int,
    ):
        self.log.info(
            "aggregation_complete",
            total_raw=total_findings,
            duplicates_removed=deduplicated,
            final_findings=final_count,
        )

    def _calculate_cost(
        self, input_tok: int, output_tok: int
    ) -> float:
        return (input_tok * 2.50 / 1_000_000 +
                output_tok * 10.00 / 1_000_000)

using Microsoft.Extensions.Logging;

public class ReviewTracer
{
    private readonly ILogger _logger;
    private readonly string _reviewId;
    private readonly string _prId;

    public ReviewTracer(
        ILogger<ReviewTracer> logger,
        string prId)
    {
        _logger = logger;
        _reviewId = Guid.NewGuid().ToString();
        _prId = prId;
    }

    public void TraceAgentCall(
        string agent,
        string filePath,
        int inputTokens,
        int outputTokens,
        int findingsCount,
        double durationMs)
    {
        _logger.LogInformation(
            "Agent review complete: " +
            "{Agent} on {FilePath} " +
            "({InputTokens}in/{OutputTokens}out) " +
            "found {FindingsCount} issues in {Duration}ms " +
            "cost ${Cost:F4}",
            agent, filePath,
            inputTokens, outputTokens,
            findingsCount, durationMs,
            CalculateCost(inputTokens, outputTokens));
    }

    public void TraceAggregation(
        int totalFindings,
        int deduplicated,
        int finalCount)
    {
        _logger.LogInformation(
            "Aggregation complete: " +
            "{Total} raw -> {Deduped} removed -> " +
            "{Final} final findings",
            totalFindings, deduplicated, finalCount);
    }

    private static double CalculateCost(
        int inputTok, int outputTok)
        => inputTok * 2.50 / 1_000_000 +
           outputTok * 10.00 / 1_000_000;
}

Key metrics to monitor in production:

• False positive rate per agent. Track when developers dismiss AI findings. If the security agent's dismissal rate exceeds 30%, the prompt needs tuning.
• Token usage per PR size. Catch PRs that blow past your budget before the bill arrives.
• Agent latency. If one agent consistently takes 3x longer than others, it may be hitting rate limits or processing too many chunks.
• Finding distribution. If 90% of findings are "style" and 0% are "security," either the codebase is very secure or the security agent is underperforming.

Technology Choices: Python vs C#

Python Implementation

Why choose Python: If your team writes Python, you get access to the richest AI/ML ecosystem available.

• Library ecosystem — LangGraph, LangChain, unidiff, and hundreds of code analysis libraries
• Rapid prototyping — Quick iteration on prompts and agent logic with Jupyter support
• Community — Most AI tutorials, examples, and StackOverflow answers are Python-first
• Git integration — GitPython, PyGithub, and unidiff provide mature diff parsing

C#/.NET Implementation

Why choose C#: If your backend is .NET, you get first-party Microsoft support and enterprise patterns out of the box.

• Native Azure integration — Semantic Kernel is Microsoft-maintained with first-party Azure OpenAI SDKs
• Enterprise patterns — Dependency injection, strong typing, and established testing frameworks
• CI/CD integration — Native Azure DevOps integration for triggering reviews on PR events
• Type safety — Record types for findings and results catch structural issues at compile time

Azure Infrastructure

Here's what you need to run this in production on Azure:

Azure AI Foundry Agent Service

Azure AI Foundry Agent Service is now generally available, providing managed orchestration for AI systems like this one.

• Built-in routing and workflow orchestration
• Managed state persistence between review steps
• Native Azure OpenAI integration
• Observability through Azure Monitor

If you're building on Azure, Foundry Agent Service can replace the custom LangGraph/Semantic Kernel orchestration layer. The trade-off is less control over the execution graph in exchange for less infrastructure to manage.

Check Azure AI Foundry Agent Service for current pricing and availability.

When NOT to Use AI Code Review

Not every team needs this. Being honest about that is more useful than pretending AI review is universally beneficial.

Simpler Alternatives

Before building a full multi-agent review system, consider whether these simpler approaches solve your problem:

• Enhanced static analysis. Tools like SonarQube, Semgrep, and CodeQL catch many of the same issues without LLM costs. If your problem is mostly security and style, start here.
• Single-prompt review. A single GPT-4o call with a well-crafted review prompt covers 70% of what the multi-agent approach does, at 25% of the cost. Start with this and upgrade when you hit its limits.
• Review checklist automation. Simple CI checks that verify "does this PR have tests?", "did error handling change?", and "are there new dependencies?" solve many review-quality issues without AI.

Key Takeaways

• Cost is negligible. At $0.05-$0.25 per review, the token cost is a rounding error compared to developer time saved. The real costs are infrastructure and maintenance.
• Observability is non-negotiable. Without tracing every agent call, you can't debug missed findings or tune prompts. Build structured logging from day one.
• Start simple. A single-prompt review gets you 70% of the value. Add the multi-agent architecture when you need specialized tuning for different review concerns.
• Manage false positives actively. Track dismissal rates per agent. If an agent's findings are ignored more than 30% of the time, fix the prompt or reduce its sensitivity.
• AI review augments, not replaces. The best results come from AI handling the mechanical first pass so human reviewers focus on architecture, design, and mentoring.

This article covers production considerations for AI-assisted code review. Actual costs and performance will vary based on your codebase size, PR patterns, and Azure region.