INFO~TECH RESEARCH GROUP

Defend Against Defects and Technical Debt in Your AI-Generated Code

Establish guardrails that keep AI‑generated code understandable, maintainable, and production‑ready.

Analyst Perspective

AI accelerates delivery; discipline preserves quality.

A common concern among junior software developers is that they’ll be made obsolete by artificial intelligence; 66% of developers worry about AI replacing humans in development workflows (Boundev Team, 2026). But the risk isn’t that developers become irrelevant, it’s that teams start treating generated output as a completed product.

AI will almost always solve a problem faster than a developer, but it does not always solve a problem correctly within a specific context. Speed is not the same as understanding. In practice, AI can generate code that looks right but quietly breaks requirements, violates architectural constraints, or introduces security and maintenance risk.

AI is here to stay. The real question is not whether you use it but whether you use it responsibly. By implementing necessary guardrails on your AI tools, you can ship reliable products and put your developers in the best place to succeed.

Caleb Pittman
Research Specialist, Applications Delivery and Management
Info-Tech Research Group

Executive summary

Your Challenge

• AI makes defects and technical debt less visible. AI-generated code can look polished and complete, which reduces healthy skepticism and delays the discovery of issues until later in the cycle.
• AI scales complexity and bad decisions faster than teams can correct them. Small prompting gaps and weak constraints get amplified into repeated patterns across features, making rework and refactoring more expensive over time.
• AI can erode consistency across the codebase. When AI produces code without a consistent set of nonfunctional requirements and coding standards, inconsistent security, performance, and reliability become embedded.

Common Obstacles

• Developers overrely on AI-generated output. Teams can treat AI-generated code as correct by default, reviewing it less
rigorously than code written by humans.
• Organizations lack clear constraints and guardrails for AI use. Without explicit requirements, standards, and boundaries, AI-generated code may reflect implicit assumptions rather than deliberate design decisions.
• Coding standards are enforced inconsistently. When standards are applied unevenly or bypassed for AI-generated code, quality varies across teams and defects accumulate in ways that are hard to detect and correct.

Info-Tech’s Approach

The solution isn’t to use AI less but to use it responsibly:

• Establish how you’ll use AI-generated code in your codebase. Clearly define where and how AI-generated code will be implemented within the software development lifecycle (SDLC).
• Implement necessary protections. Reinforce nonfunctional requirements and define prompting standards.
• Create a roadmap. Define a clear Now, Next, Later plan that translates guardrails and standards into concrete actions and adoption milestones.

Info-Tech Insight

AI-generated code increases the likelihood that defects and technical debt will accumulate faster. Put AI behind guardrails with explicit constraints and verification checks. Without these necessary guardrails, faster code delivery just means faster debt delivery.

Use AI-generated code responsibly

Your guardrails are influenced by three key dimensions.

• Degree of AI Autonomy

  How much decision-making responsibility has been delegated to AI in the creation or modification of code.

• The potential organizational impact if the code behaves incorrectly, degrades over time, or accumulates hidden technical debt.

  The potential organizational impact if the code behaves incorrectly, degrades over time, or accumulates hidden technical debt.

• Delivery Maturity

  How effectively the delivery team operates today, including its ability to collaborate, follow consistent practices, and deliver maintainable code.

Compared to human-written code, AI-coauthored pull requests (PRs) carry 1.7x more issues, 75% more logic errors, and 2.74x more security vulnerabilities (CodeRabbit, 2025).

Velocity without discipline compounds risk. Place your AI code generation tools behind explicit guardrails.

AI is transforming how organizations code

• AI-powered code generation tools can produce code automatically based on certain inputs and parameters, such as natural language descriptions. This approach can generally fall into two main categories: code completion and code suggestions. Both categories enhance code development and code efficiency (GitLab, 2024). These tools help reduce the manual work that human developers must accomplish, freeing up their time to work on more meaningful and complex tasks.
• AI code completion, an advanced code generation feature, is used in integrated development environments (IDEs) and code editors to provide automated suggestions for completing code based on context, prior code, and best practices. This capability aims to speed up the coding workflow and reduce errors by predicting the most likely pieces of code to be written next, often by suggesting variable names, method signatures, and other language-specific constructs.

“In the fast-moving world of AI, today’s tools are tomorrow’s history – because AI is now building the tools that build AI.” – Ryan Favro, Founder, Gravy.ai

Download the AI-Assisted Code Generation Tools blueprint

Case study

Industry: Finance | Source: Info-Tech Research Group

Clearwater Analytics accelerates development and improves code quality with Windsurf's AI coding assistant.

• Windsurf, formerly Codeium, partnered with Clearwater Analytics to enhance its SDLC using AI-driven tools. By integrating Windsurf’s intelligent coding assistant, Clearwater empowered its developers to maintain deep focus, reduce repetitive tasks, and improve code quality. The platform facilitated better collaboration across teams, minimized bugs early in the development process, and enforced consistent coding standards. These improvements led to faster development cycles and more efficient delivery of high-quality software.
• Windsurf also helped break down knowledge silos, enabling developers to contribute more effectively across the codebase and support Clearwater’s innovation and growth objectives.

Results

• Clearwater saw a 30% increase in developer productivity and 40% faster feature delivery. Bug rates dropped significantly due to early detection and consistent code quality. Team collaboration and cross-functional contributions improved across the board.

Insight summary

Info-Tech Insight

AI-generated code increases the likelihood that defects and technical debt will accumulate faster. Put AI behind guardrails with explicit constraints and verification checks. Without these necessary guardrails, faster code delivery just means faster debt delivery.

• Step 1

  AI is often better at writing code than individual developers, but it usually does not fully understand business context, constraints, or trade-offs. Start with a clear problem definition before involving AI. When intent is vague or poorly understood, AI will confidently optimize for the wrong outcome, producing technically sound code that fails to solve the real problem your team is accountable for.

• Step 2

  AI tools frequently produce unnecessary complexity. Constrain AI outputs to the simplest correct design and reject complexity that isn’t justified by the ticket. The rules of development don’t change just because AI is being used. Standards for simplicity, maintainability, and justified design still apply – and developers should remain accountable for enforcing them.

• Step 3

  Adopting AI-generated code is not a one-time configuration – it requires deliberate, phased implementation across the SDLC. Define clear adoption goals, success metrics, and a staged rollout plan before integrating AI-generated code. Teams that treat AI-generated code as an ongoing process are better equipped to identify failure points early and course-correct before technical debt accumulates.

Info-Tech’s methodology for defending against defects and technical debt in your AI-generated code

Key deliverable:

AI Code Quality Starter Kit

Use Info-Tech’s template to capture your desired delivery goals, AI guardrails, and success metrics.

Blueprint benefits

Define the Necessary Guardrails for Your AI-Generated Code

• Clearly define where and how AI-generated code is used across the SDLC.
• Identify high-risk SDLC steps through SWOT and root cause analyses.
• Embed nonfunctional requirements, prompting standards, and PR verification checks directly into daily development workflows.

Notable Impacts

• Reduce defect leakage from AI‑generated code.
• Improve code consistency, maintainability, and security.

Roadmap the Implementation Timeline of Your Necessary Guardrails

• Translate AI guardrails into a practical Now, Next, Later implementation plan aligned to organizational maturity.
• Connect guardrails to measurable objectives and success metrics over one‑, three‑, and six‑month horizons.

Notable Impacts

• Gain clear visibility into progress and value through defined milestones, benchmarks, and metrics.
• Enable sustainable long‑term use of AI‑generated code by operationalizing guardrails.

Tool selection is your foundation

Why your AI code tool choice predetermines quality, risk, and long-term maintainability.

Tool selection is the foundation of AI-generated code quality; the tool you choose will implicitly set the baseline for coding standards, maintainability, and security across everything it produces. Before designing guardrails or quality controls, you must first select the right AI code generation tool. A poor choice can undermine every downstream control you put in place.

To help you decide, you can read through Info-Tech’s AI-Assisted Code Generation Tools buyers guide and market trends report. Phase 1 will help you contextualize the AI code generation market by exploring key trends, future possibilities, a vendor comparison, and a SWOT analysis. Phase 2 will help you select an AI code generation tool by constructing a solid business case based on high-value use cases that are aligned to organizational goals.

Key Research

AI-Assisted Code Generation Tools

Use this comprehensive buyers guide to understand trends and future market direction, compare leading vendors, assess strengths and risks, and select an AI code generation tool.

0.1 Document your selected AI code generation tools

1-2 hours

Input: Existing knowledge of your AI-generated code tools and practices

Output: Documented rationale for AI code generation tool selection, A clear definition of intended use, constraints, and risk assumptions

1. If you have not yet selected which AI tools or vendors you’ll use to generate code, first read through and refer to Info-Tech’s AI-Assisted Code Generation Tools, which acts as a prerequisite for this blueprint.
2. Using your current knowledge of your organization’s selected AI code generation tools and vendors, write your answers to the following questions on the next slide.
   1. What AI code generation tools are being used?
   2. What are the primary purposes of these tools (new code generation, refactoring, testing)?
   3. Where in the development workflow will these tools mainly be used?
   4. What types of code or repositories are these tools permitted to generate code for?
   5. What known risks or limitations were accepted when selecting these tools?
3. Enter your answers into the AI-Generated Code Tool Snapshot section of your AI Code Quality Starter Kit.

Download the AI Code Quality Starter Kit

0.1 Document your selected AI code generation tools (continued)

Example Answers:

• What AI code generation tools are being used?

  • GitHub Copilot (IDE plugin only) and ChatGPT

• What are the primary purposes of these tools (new code generation, refactoring, testing)?

  • Developer productivity support for code suggestions, small utility functions, and refactoring assistance

• Where in the development workflow will these tools mainly be used?

  • Local development within developers’ IDEs prior to manual code review

• What types of code or repositories are these tools permitted to generate code for?

  • Nonproduction repositories only, including internal tools, prototypes, and low‑criticality services

• What known risks or limitations were accepted when selecting these tools?

  • Potential for insecure or inconsistent code patterns, mitigated by limited scope, mandatory code review, and exclusion from critical production systems

Step 1

Establish Tool Usage

Activities

• 1.1 Scope out the usage of AI-generated code in your SDLC
• 1.2 Detail your motivations for using AI-generated code

This step involves the following participants:

• Software developers (junior/senior)
• Tech leads
• Engineering/development managers
• Development directors
• Solution architects
• QA/test engineers
• DevOps/SRE managers
• Technology executives

Outcomes of this step

• A table outlining the usage of AI-generated code in your SDLC
• The main motivations driving your team to expand its usage of AI-generated code

Defend Against Defects and Technical Debt in Your AI-Generated Code

Info-Tech Insight

Step 1

AI is often better at writing code than individual developers, but it usually does not fully understand business context, constraints, or trade-offs. Start with a clear problem definition before involving AI. When intent is vague or poorly understood, AI will confidently optimize for the wrong outcome, producing technically sound code that fails to solve the real problem your team is accountable for.

Strengthen your development workflow with AI-driven coding capabilities

• Test Generation

  Test generation tools automatically create unit tests or test cases by analyzing source code or specifications, increasing test coverage.

• Document Generation

  These tools produce comprehensive code documentation or inline comments by extracting information from code or developer input.

• Low-Code/No-Code Platforms

  Low-code and no-code platforms enable users to design and deploy applications using visual interfaces and minimal handwritten code.

• API Integration

  API integration tools generate code to connect software applications with external APIs, often from specifications or prompts.

• Code Review & Refactoring

  Automated code review and refactoring tools analyze codebases to identify issues in readability, maintainability and security, offering actionable suggestions.

• Code Translation

  Code translation tools convert source code from one programming language to another, preserving logic and functionality across languages.

• Code Synthesis

  Code synthesis tools automatically generate code structures, such as functions or classes, from high-level specifications or natural language descriptions.

• Code Completion

  Code completion tools provide intelligent, context-aware suggestions as developers type, helping to finish statements and reduce syntax errors.

Understand the benefits of AI-assisted code generation tools across high-value use cases

Download the AI-Assisted Code Generation Tools blueprint

AI tools deliver strongest value from design to deployment in the SDLC

To develop your SDLC process in alignment with AI capabilities, start here: Evolve Your Software Development Lifecycle Into a Solution Delivery Lifecycle

1.1 Scope out the usage of AI-generated code in your SDLC

30-60 minutes

Input: Your current SDLC steps, Your planned AI-assisted code generation usage

Output: A table outlining the usage of AI-generated code in your SDLC

1. Map out the SDLC steps that your team currently follows.
2. For each SDLC step you include, document how AI-generated code will be used by answering the following:
   1. Who is going to be using AI-generated code?
   2. What is going to be done with AI-generated code?
   3. How will AI-generated code best be used?
3. Of these SDLC steps, which ones should be the highest priority? Consider risk exposure, the amount of autonomy you want your AI tools to have, and your organizational maturity.
4. Enter all information into the AI-Generated Code Within the SDLC section of your AI Code Quality Starter Kit.

Download the AI Code Quality Starter Kit

1.1 Scope out the usage of AI-generated code in your SDLC (continued)

Example Answers