A background coding agent is an AI-powered tool that works autonomously to write, fix, or refactor code based on a natural language task. Unlike an in-editor assistant that provides real-time suggestions, a background agent functions more like a junior developer. You assign it a well-defined task, like a bug report in a GitHub issue, and it independently works on a solution, culminating in a draft pull request (PR) ready for your review. This “fire and forget” workflow allows developers to delegate straightforward coding tasks and focus on more complex architectural and product problems.

The journey from a task description to a draft PR typically follows a clear, automated sequence. While specifics vary between tools like GitHub Copilot Workspace and others, the core workflow remains consistent and is designed to integrate directly into existing development practices.

1. Task Definition The process starts with a human developer creating a clear, detailed task, usually in the form of a GitHub issue. A high-quality issue includes a description of the bug or desired enhancement, steps to reproduce it, and the expected outcome.
2. Agent Activation The developer then invokes the agent, often by mentioning it in a comment on the issue (e.g., @github-copilot fix this). This action triggers the agent to read the issue context and start its work.
3. Codebase Analysis and Planning The agent securely clones the repository and analyzes the relevant parts of the codebase to understand the context. It formulates a step-by-step plan to address the task, identifying which files to create, modify, or delete.
4. Code Generation and Modification With a plan in place, the agent executes it by writing new code and modifying existing files. It runs builds, tests, and linters to validate its changes, often attempting to fix any issues that arise in a loop of self-correction.
5. Pull Request Creation Once the agent is confident in its solution and all checks pass, it commits the changes to a new branch. It then opens a pull request, complete with a descriptive title and a summary of its plan and the changes it made, and assigns it to the original developer for review.

Background agents excel at tasks that are well-defined and don’t require deep, abstract architectural knowledge. They are most effective when used for incremental improvements and fixes rather than brand-new, complex features.

• Targeted Bug Fixes: Fixing bugs with clear, reproducible steps and a limited scope.
• Code Refactoring: Modernizing syntax, improving variable names, or extracting methods to reduce complexity.
• Dependency Updates: Updating libraries and automatically fixing the minor breaking changes that often result.
• Boilerplate Generation: Creating the initial file structure and code for a new component or API endpoint based on established patterns in the codebase.
• Improving Code Quality: Adding comments, generating documentation, or adding tests to existing code to improve maintainability.

These use cases allow agents to handle the “grunt work,” freeing up developer time for more strategic and creative problem-solving.

Giving an AI agent autonomous access to your codebase introduces new challenges, particularly around security, permissions, and code quality. Understanding these risks is the first step to mitigating them effectively.

A primary concern is that an agent might inadvertently introduce security vulnerabilities or logical flaws. Because the agent doesn’t understand the full business context of the application, its solution might be technically correct but functionally wrong.

Another significant challenge is managing permissions. Granting an AI write access to your source code requires a thoughtful approach to security to prevent unintended or malicious changes.

Mitigation strategies:

• Scoped Permissions: Limit the agent’s access to only the repositories it needs to work on. Avoid granting organization-wide access.
• Branch Protection Rules: This is a non-negotiable best practice. Configure your repository to require human review and approval on all pull requests. Enforce status checks, ensuring that all tests and security scans must pass before an AI-generated PR can be merged.
• Human-in-the-Loop: Always treat the agent’s output as a draft. The developer who assigned the task is ultimately responsible for reviewing every line of code, testing the changes, and ensuring the solution is robust and secure.

While a coding agent works on your application’s source code, your overall development ecosystem includes numerous other tools, APIs, and services that also need protection. For example, a CI/CD pipeline might need to interact with a secure staging environment, or a custom script might need to access an internal metrics API. This is where a robust authentication and authorization platform becomes critical.

Kinde provides the tools to secure these machine-to-machine (M2M) interactions. You can protect your internal APIs and services, ensuring that only authorized applications and agents—each with their own unique credentials—can access them. By using Kinde to manage permissions for these backend services, you can create a secure, auditable development environment where every part of your workflow is protected, from the first line of AI-generated code to the final deployment.

• Protect an API
• Registering Machine to Machine applications