Beyond Code Generation: How Continue Enables AI Code Review at Scale

Brian

• 4 min read
Beyond Code Generation: How Continue Enables AI Code Review at Scale

Generative coding started with autocomplete and has evolved into sophisticated code generation. But what if your AI assistant could do more than just write code? What if it could reason about your codebase, enforce standards, and provide intelligent feedback? This is where Continue transcends its identity as a coding assistant and becomes a platform for custom AI agents.

While most developers know Continue for its excellent code completion and chat features, its true power lies in its extensible architecture. Continue isn't just another copilot—it's a framework for building tailored AI experiences that understand your specific context, conventions, and requirements. Want to see this in action? Check out my CodeBunny project, a privacy-first PR review bot built on Continue's agent capabilities.

Configuration as Code: The Continue Advantage

Continue's configuration system is what sets it apart from monolithic AI tools. Through the Continue Hub and simple configuration files, you can leverage pre-built agents or create your own:

Use Pre-Built Agents from the Hub Continue Hub hosts ready-to-use agents with integrated tools and configurations. For example, the review-bot config provides a complete PR review setup with built-in MCP tools:

# Use a hub agent directly
cn --config continuedev/review-bot

# Or set it as your default
cn config set continuedev/review-bot

Create Project-Specific Rules Unlike generic AI assistants, Continue allows you to define rules that matter to your team. Place markdown files in .continue/rules/ to establish conventions:

---
globs: "**/*.{ts,tsx}"
description: "React Best Practices"
---
- Prefer functional components with hooks
- Memoize expensive computations
- Keep components under 200 lines

These rules aren't just documentation—they become part of your AI's reasoning process through Continue's rule system, ensuring feedback aligns with your team's standards. You can even pull additional rules from the hub using cn --rule nate/spanish or configure them in your workspace.

Leveraging Claude Opus for Deep Code Understanding

Claude Opus 4.1 brings unprecedented reasoning capabilities to code review. Its ability to understand complex relationships, architectural patterns, and subtle bugs makes it ideal for tasks that go beyond simple generation:

  • Architectural Analysis: Opus can identify violations of separation of concerns, suggest better design patterns, and spot potential scalability issues
  • Security Reasoning: Beyond finding obvious vulnerabilities, Opus understands attack vectors and can suggest defense-in-depth strategies
  • Test Coverage Intelligence: Not just counting lines, but understanding what edge cases need testing based on code logic
  • Performance Optimization: Identifying algorithmic complexity issues and suggesting more efficient approaches

The beauty of Continue is that you can leverage these capabilities through its extensible platform while maintaining complete control over how and where your code is processed.

Building Custom Agents with Continue CLI

Continue's CLI (cn) opens up possibilities for automation that go far beyond IDE integration. The PR review bot guide demonstrates how to build sophisticated workflows:

# Install the CLI globally
npm i -g @continuedev/cli

# Use the review-bot agent from the hub
cn --config continuedev/review-bot

# Apply custom rules on top of hub agents
cn --config continuedev/default --rule security-standards

# Headless mode for automation
cn -p "Review this code for security issues" < changes.diff

The hub agents come with pre-configured MCP (Model Context Protocol) tools, allowing you to connect to external data sources and services without manual setup. This isn't just running prompt, it's orchestrating complex reasoning workflows that understand your entire codebase context through integrations with databases, documentation systems, and custom tools.

See how I made my own Codebunny

https://github.com/bdougie/codebunny

Privacy-First Intelligence

One of Continue's most compelling features is its flexibility in deployment. You can:

  • Use Continue's cloud service for convenience
  • Self-host for complete data sovereignty
  • Run entirely in CI/CD environments like GitHub Actions
  • Mix and match approaches based on sensitivity

This architecture means you can have AI reasoning about your most sensitive code without ever sending it to third-party servers. Your configurations and rules remain portable across all deployment options.

Real-World Impact

Teams using Continue for reasoning tasks report:

  • Consistency at Scale: Custom rules ensure every PR gets reviewed against the same standards
  • Knowledge Transfer: New developers learn team conventions through AI feedback
  • Focus on Complex Problems: Automated reasoning handles routine checks, letting humans focus on architecture and design
  • Continuous Improvement: Rules evolve with your codebase, capturing lessons learned

Getting Started with AI Reasoning

Transform Continue from a coding assistant to a reasoning engine:

  1. Install Continue CLI: npm i -g @continuedev/cli
  2. Choose a Hub Agent: Browse hub.continue.dev for pre-configured agents
  3. Define Your Rules: Create team-specific rules in .continue/rules/
  4. Automate Workflows: Use cn in CI/CD for automated analysis

Start with simple commands like cn for interactive mode, or use cn -p "your prompt" for scripting and automation. The hub provides agents with pre-configured MCP tools for databases, documentation systems, browser automation, and more, no manual setup required.

The Future is Configurable

Continue represents a fundamental shift in how we think about AI in development. Instead of adapting to generic tools, we're configuring AI to understand our specific contexts, conventions, and requirements. This isn't just about better code completion, it's about AI that reasons like a senior developer on your team.

As AI models become more powerful, the ability to configure and extend them becomes crucial. Continue's open architecture ensures you're not locked into any particular model or deployment strategy. Whether you're using Opus today or whatever comes tomorrow, your configurations and rules remain yours.

The question isn't whether AI can write code—it's whether AI can think about code the way your team does. With Continue, the answer is finally yes.

Check out our guide on building your AI code review agent.