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Help developper in prototyping, refactoring or rewriting specific part of the application using AI coding agents in a controlled way.
With the recent emergence of highly efficient AI coding agents, developers now have tools capable of understanding existing codebases, interpreting specifications, and assisting in prototyping, refactoring, or rewriting specific parts of an application. These AI agents can generate code suggestions, automate repetitive tasks, and help improve maintainability, while accelerating development cycles.
In a controlled setting, the AI coding agent operates under strict supervision to ensure code quality, adherence to architectural standards, and compliance with security and performance requirements. Human oversight remains central, with developers reviewing, validating, and integrating the AI-generated outputs into the application, ensuring that the assistance remains reliable and predictable.
Activities in an AI-assisted coding service
- Selecting the right AI coding tool: evaluate the available options, considering commercial offerings versus open-source solutions. Identify and assess candidates based on key criteria such as ease of use, customization, integration capabilities, deployment options, security, sustainability, and cost to ensure the tool fits the team’s needs and long-term modernization strategy.
- Defining supervision guidelines: establish processes to provide clear and precise task descriptions for the AI, monitor its performance to detect when it loses efficiency or produces suboptimal results, and guide the AI outputs to ensure alignment with coding standards, architectural constraints, and functional requirements.
- Prototyping and experimentation use AI agents to rapidly explore design alternatives or implement experimental features in a sandboxed environment, accelerating proof-of-concept development.
- Refactoring and rewriting assistance: Identify code sections suitable for AI intervention and manage incremental improvements, ensuring that functionality is preserved while code quality and maintainability are enhanced.
- Integration support: assist developers in validating the architecture at functional, performance, and security levels. Set up testing frameworks and version tracking, and ensure smooth integration with other modules to maintain consistency and reliability across the application.
What is the typical effort for this activity ?
The effort depends on the scenario (prototyping vs full development), the available input (running system, spec, code,...). The effort can be estimated using traditional effort estimation techniques. A speed up factor can be anticipated provided the AI is correctly guided, however this factor is not easy to anticipate given the short and quick evolution of this usage. The effort will be more accurately estimated after the Diagnosis during the Planning phase.
What are the possible dependencies with other activities ?
The presence of good documentation at code level and specification level is highly recommended to make sure the developer can provide clear prompts to the coding agent and check the result. Result should also be checked with other techniques like running available or updated test suites and static code analysis.
What expertise is required to achieve this activity ?
To avoid wild vibe coding, the user should have:
- software engineering skills that will allow him to provide clear directive to the coding agent, keep control of the architecture, identify when it is diverging
- AI literacy to understand the capabilities and limitation of the technology, write clear precise prompts and keep control of the agent
Is this activity optional, recommended or mandatory?
Optional, it depends on the kind of system and of the modernization constraints, and strategy adopted.
Is there a recommended methodology to support this activity ?
The methodology is part of the proposed tasks and cover tool selecting, guidance and integration guidelines.
What is therecommended tooling for this activity ?
The spectrum of tool is very large and evolving, tool selection is part of the activity and of the proposed support.
What are the benefits of using an AI for development ?
- Productivity / Efficiency: AI-assisted development lets developers concentrate on business logic rather than boilerplate coding. It leverages knowledge of existing components and frameworks to generate or suggest code, speeding up implementation. This ensures consistency with architectural standards while reducing errors.
- Cost Reduction: this follows from increased productivity and less human effort but also to lower maintenance costs as AI is generating code that is consistent with coding standards, implements proper exception handling, and follows best practices. However, security remains a critical concern: AI-generated code must be carefully reviewed and monitored to ensure compliance with security policies and to avoid introducing vulnerabilities.
- Agility / Flexibility: rapid iterations: AI enables faster prototyping and iterative development, allowing teams to test alternatives quickly. Continuous feedback loops with clients ensure delivered solutions meet business requirements. Rapid iterations reduce the risk of late-stage changes.
- Accessibility: AI-assisted coding can lower the barrier for non-technical contributors to participate in design or workflow automation. Client teams can provide specifications, validate functionality, or prototype processes, fostering collaboration and alignment between IT and business.
What are the limitations of using an AI for development ?
- Software engineering and control: AI coding agents should never be left to operate without supervision. Unlike human developers, they can lose sight of the overall architecture or project goals, potentially introducing regressions or inconsistencies. Careful governance, code review, and integration checkpoints are essential to maintain control and ensure that AI contributions align with the intended design and quality standards.
- Limited contextual awareness: current AI models suffer from limited memory of prior actions, meaning they often cannot retain a full view of the project or previous code changes. This can result in suggestions that conflict with existing logic, duplicate functionality, or break dependencies, requiring vigilant oversight and incremental integration to prevent regressions.
- Security is not guaranteed: AI does not inherently prioritize security. Generated code may introduce vulnerabilities or fail to enforce secure coding practices. It is therefore critical to perform security reviews, automated scanning, and testing on all AI-assisted outputs.
- Code confidentiality: to protect sensitive code, some organizations may need to host AI tools internally. While this improves data security, it can reduce AI performance and the richness of suggestions, as internal models may lack the same training data or updates available in cloud-based solutions. This trade-off between confidentiality and performance/quality must be carefully managed.