A/B testing for an AI coding agent is a controlled experiment that measures its performance against a human developer or a developer working without the agent. The goal is to gather objective data on whether the agent improves key development metrics. Instead of relying on gut feelings or generic benchmarks, you create a head-to-head comparison within the context of your own projects, codebases, and team workflows.

This approach is especially valuable for “background agents”—AI tools designed to autonomously pick up tasks from a backlog, such as fixing bugs, refactoring code, or adding small features. By setting up a fair test, you can make an informed decision about the agent’s return on investment (ROI) before rolling it out more broadly.

A well-designed experiment removes guesswork and provides clear, actionable insights. The core idea is to create two groups—a control group (your standard process) and a treatment group (your process with the AI agent)—and compare their results on a similar set of tasks.

Here is a simple, four-step framework for setting up your test:

1. Define a clear hypothesis. Start by stating what you believe the agent will achieve. A good hypothesis is specific and measurable. For example: “The AI agent will reduce the average pull request (PR) cycle time for bug fixes by 20% without increasing the defect escape rate.”
2. Select a curated issue set. The quality of your test depends on the tasks you select. Choose a set of well-defined, similarly-sized issues from your backlog. Good candidates include bug reports, minor feature enhancements, or tech debt tickets. The key is that the tasks are representative of the work you expect the agent to handle.
3. Establish control and treatment groups. Divide the curated issues into two batches.
   • Control Group (A): Assign these issues to developers to complete using their normal workflow.
   • Treatment Group (B): Assign these issues to the AI agent. A human developer should still be in the loop to review and merge the agent’s work.
4. Track the right metrics. To measure ROI, you need to collect quantitative and qualitative data. Focus on metrics that align with your hypothesis.

These metrics provide a balanced view of the agent’s impact, covering speed, quality, and efficiency.

Public benchmarks like SWE-bench are excellent for understanding an agent’s general capabilities. However, they can’t tell you how an agent will perform within the unique constraints of your environment. An AI that excels at solving standalone algorithm problems might struggle with your company’s internal libraries, complex build processes, and specific coding conventions.

By testing the agent on your own repositories, you get a true measure of its effectiveness. This tailored approach answers critical, practical questions:

• Can the agent navigate your monorepo and understand service dependencies?
• Does it adhere to your team’s linting rules and style guides?
• Can it generate code that passes your custom integration tests?
• Does it understand the business context embedded in your proprietary code?

Answering these questions is the only way to know if an agent will be a productive member of your team or just a source of noise and rework.

Running a fair experiment isn’t always straightforward. Being aware of common pitfalls can help you design a more robust test and trust the results.

• Issue Selection Bias: It’s easy to subconsciously pick tasks that you know the AI will excel at, leading to overly optimistic results.
  • Solution: Create a pool of eligible, well-defined tickets and randomly assign them to the control and treatment groups.
• Measuring Code Quality: Quality is notoriously subjective and hard to measure.
  • Solution: Use a combination of objective and subjective measures. Track automated metrics from static analysis tools (e.g., code complexity, test coverage) alongside the defect escape rate and qualitative feedback from code reviewers.
• The Hawthorne Effect: Your team might work differently or more diligently simply because they know they’re being observed.
  • Solution: Frame the experiment as a collaborative effort to evaluate a new tool, not as a performance review. Emphasize that the goal is to improve the team’s workflow, not to compare individuals.
• Insufficient Data: A test that runs for only a few days with a handful of tickets may not be statistically significant.
  • Solution: Plan to run the experiment for at least a full sprint, or ideally longer. The more data points you collect, the more confident you can be in the outcome.

• Start with low-risk tasks. Before letting an agent work on critical features, have it tackle lower-stakes issues like updating documentation, upgrading dependencies, or fixing non-critical bugs.
• Keep a human in the loop. The goal of a background agent is to augment your team, not replace human oversight. Every piece of code generated by the agent should be reviewed by a developer before being merged.
• Automate metric collection. Manually tracking metrics is tedious and error-prone. Use your existing DevOps toolchain—Git history, CI/CD pipelines, and project management software APIs—to automate data collection.
• Communicate clearly and transparently. Get your team’s buy-in by explaining the what, why, and how of the experiment. Transparency builds trust and encourages valuable feedback that can help you refine your evaluation process.

While Kinde doesn’t measure code output, it provides foundational tools that can support the workflows and automation around your A/B test. Secure and streamlined development processes are critical for getting clean, reliable data.

For example, you can use Kinde’s feature flags to control which developers or environments have access to the AI agent’s capabilities, allowing for a phased rollout or controlled experiment. You could create a flag that enables the agent for a specific “treatment group” of users.

Additionally, the scripts and internal tools you build to automate metric collection need to be secure. Kinde allows you to secure access to your internal APIs using standards-based authentication, ensuring that only authorized services can report or access experiment data. This is especially useful for machine-to-machine (M2M) applications, like a script that pulls data from your Git server and pushes it to a metrics dashboard.

• About feature flags
• Manage everything with the Kinde Management API
• Machine to machine (M2M) overview