Modern refactoring refers to the process of restructuring existing code without changing its behavior. It is also essential for software maintainability, readability, and performance. Recent advancements in large language models (LLMs) and reinforcement learning (RL) suggest new ways to automate and optimize refactoring. In particular, agentic AI systems can operate on codebases as virtual developers, iteratively identifying and applying refactorings to improve code quality. At the same time, RL provides a natural framework for learning code transformation strategies through trial and error. In this blog, we will review the conceptual models, foundations, and emerging frameworks that drive the RL-driven and agentic refactoring.

What is Agentic AI in Software Engineering?

Agentic AI refers to AI systems that act autonomously with goal-directed planning and decision-making. Such agents perceive their environment, reason about goals, plan actions, and learn from feedback. In a software context, an agentic code tool can explore a code repository, detect opportunities, decide on a refactoring, apply it, and then evaluate the result. IBM describes an agentic system’s “goal setting” stage, where it develops a strategy to achieve objectives, often by using “reinforcement learning or other planning algorithms. After execution, it learns and adapts through reinforcement learning or self-supervision to refine future decisions. An autonomous AI agent might coordinate multiple specialized agents for refactoring.

For instance, a recent conceptual framework envisions a multi-agent LLM environment where each agent focuses on a different concern and collaborates to propose refactoring strategies. These agents can use consensus or auction-like protocols to balance trade-offs between goals and could be orchestrated within a CI/CD pipeline. In this way, agentic AI extends traditional code generation tools into planners that perform multi-step transformations, guided by RL-based learning loops.

An Introduction to Reinforcement Learning for Code Refactoring

At its core, refactoring with RL can be formalized as a Markov Decision Process (MDP). The state is the current code base, and actions are atomic refactoring operations (like extract method, rename variable). When an agent selects an action, the code changes to a new state. A reward is then given based on code quality metrics or test outcomes. Key components of an RL framework for refactoring include:

States: representations of code (AST graphs or token embeddings).
Actions: refactoring transformations (insert/delete/replace code fragments).
Transition: applying an action yields a new code state
Reward: measures of improvement

Importantly, reinforcement learning learns through trial and error and does not require labeled input-output examples of refactorings. As one survey notes, it also provides a new approach to code generation and optimization by enabling “label-free input-output pairs” and leveraging existing knowledge through trial and error. This allows models to adapt to codebases and various objectives without exhaustive supervision.

What are Reward Functions and Code Quality Metrics?

A central challenge is designing rewards that capture “better code.” Standard reward signals include:

Compilability and Test Success: The code must compile and pass all existing unit tests. In one study, agents were rewarded for generating compilable code and for having the desired refactoring applied; RL-aligned models saw unit-test pass rates rise substantially.
Static Code Metrics: Measures like cyclomatic complexity, nesting depth, or code length (shorter is often better) can serve as proxy rewards. Lower complexity and fewer “code smells” (e.g., long methods, duplicated code) imply maintainability gains.
Similarity or Style Scores: Automated metrics such as BLEU/ROUGE/CodeBLEU can reward semantic fidelity to a reference, refactoring, or adherence to style guidelines
Domain-specific Objectives: For example, if optimizing for performance, the reward could be reduced runtime or memory usage; for security, the absence of vulnerability patterns.

Learning Code Transformations

Reinforcement learning algorithms include policy gradients (PPO), value-based methods (DQN), and search-based RL (AlphaZero/MCTS). In practice, an LLM policy is usually fine-tuned with policy gradients, and it generates refactored code, receives a reward, and updates to favor higher-reward transformations. RL techniques enable code models to iterate on their outputs. The agent creates candidate refactorings, measures their quality, and then refines its strategy. Through numerous trials, it learns which transformations preserve correctness while also boosting metrics. This self-improvement loop mirrors how developers try different approaches and learn from outcomes. Importantly, modern LLMs with RL can combine reasoning and search. Additionally, an agent might utilize its language understanding to propose a refactoring plan, and then employ reinforcement learning to optimize the execution and handle unexpected cases.

Agentic Refactoring Architectures

Agentic systems for refactoring can be single-agent or multi-agent. A single-agent LLM might sequentially propose refactorings across the codebase, using RL to update its one policy. For example, OpenAI’s Codex is described as “designed to work like a team of virtual coworkers.”. Codex operates on a user’s code repository with multiple sandboxed agents: one writes code, another runs tests, another fixes bugs, all in parallel. Codex’s underlying model (codex-1) was fine-tuned for software engineering and trained via reinforcement learning on coding tasks. In effect, Codex agents autonomously improve and refactor code according to user prompts, illustrating agent-based reinforcement learning (RL) in practice.

More ambitiously, a multi-agent LLM environment can tackle complex refactoring goals. As noted, a framework can deploy specialized agents that negotiate or vote on changes. Coordination protocols, such as consensus or auctions, ensure that they do not conflict with each other. Future work even explores multi-agent reinforcement learning, so these specialists dynamically adjust their proposals. This demonstrates how engineering teams can collaborate, replacing humans with cooperating AI agents that collectively reduce technical debt across multiple fronts.

Some crucial elements of an agentic refactoring pipeline consist of:

Perception: The agent reads code and possibly documentation, utilizing parsers or embeddings to comprehend the structure.
Planning: It identifies refactoring opportunities, such as detecting long methods via static analysis, and sequences the necessary actions.
Execution: It applies code transformations, often by editing the AST or text.
Verification: It compiles tests on the new code to verify correctness.
Learning Loop: Based on outcomes (comparable, tests passed, metric improvements), the agent updates its policy via reinforcement learning.

Each loop is like an episode in reinforcement learning. Over time, the agentic system learns to refactor by internalizing which changes yield better code. This is precisely the kind of learning and adaptation that defines AI as agents that refine their strategies through continuous feedback.

To Conclude

AI-driven code refactoring is quickly shifting from concept to real-world application. Agentic AI frameworks empower code assistants to plan, make decisions, and act autonomously. At the same time, reinforcement learning offers a structured way for these systems to learn complex code transformations through trial and error. In this context, theoretical models define refactoring as a Markov Decision Process (MDP), where the code represents the state, edits are the actions, and improvements in code quality serve as rewards. Some prominent tools, such as OpenAI’s Codex and other experimental AI agents, are already proving that this approach works at scale. The outcome is a more innovative, automated approach to analyzing, restructuring, and continuously optimizing code. Additionally, it leads to well-organized, safer, easier-to-maintain software systems without manual intervention, enabling development teams to focus on higher-value work.

Reference Site — https://www.aziro.com/blog/code-refactoring-with-agentic-ai-and-reinforcement-learning/

Code Refactoring with Agentic AI and Reinforcement Learning