Your AI coding assistant now needs its own AI reviewer
As AI coding assistants go mainstream, a silent wave of technical debt is building. Here’s how the industry is fighting back.
The AI coding revolution is here, generating entire applications from a single prompt. But this unprecedented speed has a hidden cost: a deluge of unvetted, often bloated, and buggy code. As developers try to tackle this new form of technical debt, another wave of AI is emerging: AI-powered code reviewers designed to act as the senior developer on every team, ensuring quality, security, and maintainability.
The most critical shift in software development isn’t about generating code faster; it’s about building intelligent systems of governance. We explored this evolution with Aravind Putrevu, Head of Developer Relations at CodeRabbit, a leading AI code review platform.
The hidden cost of velocity: diagnosing “AI slop”
The speed of AI code generation comes with a steep, often hidden, price in quality and maintainability. In an interview with TechTalks, Putrevu described this new form of technical debt as “AI slop.” This happens because AI coding agents often act as a “yes-master,” generating code without the critical pushback or foresight a human developer might have. For example, when asked to make a minor change, an AI might write an entirely new function but fail to remove the original, now-obsolete one. This leaves a trail of dead code, bloating the codebase and making it harder to maintain.
This problem extends beyond simple redundancy. These agents can struggle with high-level architectural concepts like modularization and refactoring, leading to code that functions but is difficult to scale or debug. The result is a quiet accumulation of technical debt. This trend of increasing bugs and architectural issues is growing as more teams adopt AI coding assistants. As Putrevu noted, the stakes are high. While a hallucination in a chatbot is an inconvenience, a flaw in code can have severe consequences. “In coding, the cost of an error is very, very high,” he said. “It can have lasting impacts if code is not reviewed and if things are not properly checked.”
The guardian at the gates: an AI to review AI
To counter this, a new category of AI tools has emerged, focusing not on generation but on review. These platforms act as automated senior engineers, analyzing pull requests with a level of depth that goes far beyond simple style checkers or linters. They examine code for complex issues like security vulnerabilities, race conditions, and performance bottlenecks. The goal is to provide feedback that addresses the core structure and quality of the code. “It’s not just about style or small changes in the code; it’s more about how well you can organize this code for better readability,” Putrevu said.
A key innovation in this space is the ability for the AI reviewer to adapt to a specific team’s culture and conventions. For instance, CodeRabbit includes a “learnings” feature where developers can correct the bot’s suggestions, teaching it the team’s specific standards. The tool records this feedback in its knowledge base and applies it to future reviews. This turns a generic AI into a tailored partner that understands the nuances of a particular project. This approach underscores a central philosophy: the goal is augmentation, not replacement. “We don’t believe AI is there to replace humans,” Putrevu said. “We are there to augment them.”
The “agentic SDLC”
This dynamic of generation and review points toward a more profound shift in software development: the rise of the agentic software development lifecycle (SDLC). The future is not a single, monolithic AI but an ecosystem of specialized agents that collaborate. At recent events like GitHub Universe, this vision began to take shape, with a move toward what Putrevu calls an “Agent HQ and a command center approach, where agents play with each other.” In this new workflow, a human developer might assign a task, an AI coding agent generates the code and submits a pull request, and an AI review agent automatically analyzes it for quality, security, and adherence to standards.
This automated loop can even extend to remediation. If the review agent finds a significant issue, it can create a new task that is then assigned to another agent to fix. For this interconnected system to work, however, major platforms will need to facilitate seamless “agent-to-agent handoff.” This interoperability is the final piece of the puzzle, allowing organizations to build robust, automated workflows that manage the rising complexity and volume of code.
How will the role of developers evolve?
The rise of AI agents has fueled a debate about whether learning to code is becoming an obsolete skill. Putrevu offered a compelling analogy to counter this idea. “We all use Tesla and self-driving cars. Do you stop learning to drive the car? No, you are still at the steering wheel,” he said. AI tools are powerful aids, but they still require a skilled operator who understands the fundamentals. The human developer’s role is shifting from a hands-on coder to a pilot or an architect.
In this new paradigm, deep technical knowledge becomes a force multiplier. An experienced developer can guide, correct, and validate an AI’s output far more effectively than a novice. “You need to understand how, what, and where to replan, revert, and steer these agents to success,” Putrevu said. “Doing that requires prior knowledge.” Rather than being replaced, senior developers are finding their expertise augmented, allowing them to oversee more complex systems and enforce higher standards of quality, with AI agents acting as their scribes and first-line reviewers.
The initial gold rush of AI code generation is giving way to a more mature understanding of its limitations. The future of sustainable software development depends on balancing rapid creation with intelligent governance. AI-powered review is becoming a foundational pillar of the modern SDLC. The future of software engineering is a partnership between humans and AI. As AI becomes the writer, the human developer becomes the editor, the architect, and the final arbiter of quality.
I use separate agents to code and review the code. Each one has different context and prompts. Together with feedback from the checker agent to the coder agent, it’s working quite well :)