An automated tech-debt burner is a system that uses an AI agent to proactively find and fix low-level issues in your codebase on a recurring schedule. Think of it as a robotic team member that handles the tedious but necessary chores of repository maintenance—like removing unused code, upgrading dependencies, or standardizing naming conventions—so your human developers can focus on building features. These “burners” typically run nightly, tackling small tasks that, if left unchecked, accumulate into significant technical debt.

A scheduled agent combines a few key components to operate autonomously within your development workflow. The process is designed to be safe, observable, and controlled, ensuring the agent helps more than it hinders.

At its core, the system relies on a scheduler, an agent, and a set of guardrails.

1. Scheduler: This is the trigger. A simple cron job or a CI/CD pipeline (like GitHub Actions or GitLab CI/CD) kicks off the agent’s task at a set time, such as 2 AM when repository traffic is low.
2. Agentic Core: This is the brain. It consists of a Large Language Model (LLM) and a script that gives it a goal. The script securely connects to your repository, instructs the agent on what task to perform (e.g., “Find and remove all functions that are exported but never imported by another file”), and provides it with tools to read, write, and analyze code.
3. Guardrails & Tools: This is the safety harness. Before committing any changes, the agent uses standard development tools to validate its work. It will run your existing test suite, use a linter to check for style issues, and may even have custom checks to ensure it doesn’t modify critical files.
4. Pull Request: Once the work is complete and validated, the agent bundles its changes into a branch and opens a pull request, clearly summarizing what it did. This allows for human review and maintains a clear history of changes.

This entire workflow transforms code maintenance from a manual, often-neglected chore into a consistent, automated process.

The ideal tasks for an agent are objective, repetitive, and have a clear definition of “done.” You can manage these tasks in a dedicated backlog, which helps you prioritize and define the scope for your automated assistant.

Here are some common categories of “agentable tasks”:

• Code Deletion: Safely removing dead code, unused variables, and commented-out logic that clutters the codebase.
• Dependency Management: Automatically upgrading package versions, running tests, and creating a PR with the release notes.
• Refactoring: Performing systematic renames, converting old syntax to modern standards, or restructuring files based on a defined pattern.
• Testing: Upgrading test runners, converting tests to a new framework, or generating boilerplate for new components.

This structured backlog turns abstract maintenance goals into concrete, actionable tasks for your agent.

Implementing an AI agent in your repository requires building trust. A phased rollout with clear guardrails ensures you can introduce automation safely and effectively, proving its value at each step.

The first week is about watching and learning. The agent should operate in a read-only or “dry run” mode, where it identifies potential changes but doesn’t act on them.

• Goal: Establish a baseline and validate the agent’s understanding.
• Actions:
  • Choose a single, low-risk task, like identifying commented-out code blocks.
  • Set up the scheduler and provide the agent with read-only access to the repository.
  • Configure the agent to post its findings to a Slack channel or in a report, rather than creating a pull request.

Now, the agent gets to make its first real changes, but under strict supervision. This is where you introduce the first critical guardrail.

• Goal: Allow the agent to create its first PRs while minimizing risk.
• Guardrail: Maximum Diff Size: Implement a check that stops the agent if its proposed changes exceed a certain number of lines (e.g., 150). This prevents unexpectedly large or complex refactors.
• Actions:
  • Give the agent write access.
  • Enable the agent to create pull requests for the task defined in Week 1.
  • Require a human developer to review and approve every PR the agent creates.

With confidence growing, you can give the agent a slightly more complex task and add another layer of automated validation.

• Goal: Trust the agent with more meaningful work, backed by automated checks.
• Guardrail: Test Thresholds: Configure the agent’s workflow to fail if its changes cause any tests to fail or if code coverage drops below a set threshold.
• Actions:
  • Introduce a second task from your backlog, such as upgrading a minor dependency.
  • Ensure the CI pipeline rigorously tests the agent’s changes.

In the final phase, the focus shifts from reviewing every line of code to managing the agent’s performance and backlog. For highly reliable tasks, you can consider allowing the agent to merge its own changes.

• Goal: Achieve true automation for proven, low-risk tasks.
• Actions:
  • Identify a task the agent has performed reliably (e.g., dead code removal).
  • Configure a workflow where the agent’s PR is auto-merged if it passes all checks: tests, linting, code coverage, and max diff size.
  • Your team’s role now evolves to defining new tasks and refining the agent’s instructions.

Automating code maintenance with AI is powerful, but it’s important to approach it with a clear understanding of its limitations and risks.

• Agents are for toil, not creativity. A common misconception is that agents will replace developers. In reality, they are tools to handle drudgery. They excel at systematic, repetitive tasks, freeing up engineers to focus on complex architecture, user experience, and creative problem-solving.
• Risk must be actively managed. Allowing an AI to write to your main branch is inherently risky. This risk is managed by starting with read-only runs and layering in guardrails like diff limits, test thresholds, and mandatory human reviews. Never give an agent more permission than it needs.
• Hallucinations are a real problem. An agent can misunderstand a task or make a mistake, just like a human. A robust test suite is your most important safety net. If your tests are comprehensive, they will catch most agent errors before they can be merged.

When your tech-debt burner agent needs to interact with other parts of your infrastructure—like a private package registry, a staging environment, or an internal API—it needs a secure identity. Hardcoding API keys or secrets in your CI/CD environment is risky and difficult to manage.

This is a classic machine-to-machine (M2M) authentication problem, and it’s where Kinde can help. Instead of treating your agent as an anonymous script, you can register it as a “machine” application in Kinde.

Using the Client Credentials Flow, a standard protocol for M2M communication, your agent can request a short-lived access token from Kinde. This token proves the agent’s identity and grants it specific, limited permissions to access other resources. For example, you can issue a token that allows it to download a package but not publish one. This approach secures your internal systems by ensuring every automated process is authenticated and authorized, just like a human user.

• Register a machine-to-machine application
• How to authenticate a machine-to-machine application
• Protecting an API