The world of software engineering is undergoing a fundamental transformation. Whereas the pace of development was once dictated by human capacity, the rise of autonomous AI agents and the concept of "vibe coding" (programming based on intuitively assigning tasks to AI) has drastically accelerated that pace. However, this increase in speed has brought unexpected side effects: a rise in error rates on the main branch and CI pipeline congestion.

According to a CircleCI report, team activity on feature branches has increased by 15%, but main branch throughput has dropped by nearly 7%. The result is a paradox: development is happening faster, but deploying functional code is slowed down by endless bug fixes that CI caught too late.

The "outer loop" problem in the AI era

To understand why CircleCI's solution matters, we need to distinguish between two concepts: the inner loop and the outer loop.

• Inner loop is the space where the developer or AI agent actively writes code, tests it locally, and fixes errors. It is an environment of immediate feedback.
• Outer loop is the CI (Continuous Integration) process that runs only after the code is pushed to the remote repository. This is where final, complex tests and builds are performed.

The problem arises when an AI agent "fires off" dozens of small commits that appear functional but fail in the real environment. By the time CI (outer loop) reports an error, the context in which the agent was working is already gone. The developer has to re-explain the task, reconfigure the prompt, and wait for another cycle. This leads to wasted compute power, LLM tokens, and, above all, precious engineering time.

How do Chunk Sidecars work?

Chunk Sidecars bridge this gap by bringing part of the validation from the outer loop directly into the inner loop. Technically, they are lightweight, remote microVMs (micro virtual machines) that act as "shadows" of your local environment or the AI agent's environment.

These sidecars enable running so-called microbuilds. This means that during the code generation process itself, the following runs:

• Linting: Checking syntactic correctness and adherence to coding standards.
• Unit tests: Running basic tests to verify logic.
• Build process: Verifying that the project can actually be compiled (e.g., using pnpm or npm).
• Repo policy hooks: Checking that the code does not violate the company's internal rules.

Thanks to this, the agent receives immediate feedback. If it tries to write faulty code, the sidecar tells it within milliseconds, and the agent can fix the error before you can even say "git push". As Loop Lab notes, this approach results in pull requests arriving at CI already nearly "green" (error-free).

Practical impact for Czech companies and developers

For the Czech tech scene, which is strongly represented by software services exports and agile startups, this solution has several direct implications:

1. Reduced AI infrastructure costs

Every failed CI cycle, where the model (e.g., Claude 3.5 Sonnet or GPT-4o) has to be "re-prompted", costs money in tokens. By implementing Chunk Sidecars, companies save costs on unnecessary API calls for fixing errors that should have been resolved locally.

2. Compliance with EU regulations (AI Act)

With the arrival of the EU AI Act, the emphasis on system reliability and safety is increasing. Companies will need to demonstrate that their software (including AI-generated code) meets certain quality standards. Tools like Chunk Sidecars enable implementing these checks directly into the development cycle, reducing the risk of subpar code ever leaving the development environment.

3. Availability and pricing

CircleCI states that Chunk Sidecars are available in Preview mode for Performance and Scale plans. However, according to recent updates, the feature is also available for Free tier users (in a limited form), allowing even smaller Czech teams and individuals to start with agent validation without high upfront investment.

Comparison: Local testing vs. Chunk Sidecars

You might be thinking: "Why not just use local tests?" The difference lies in context and consistency. A developer's local environment (or their computer) often differs from the production or CI environment (different Node.js versions, OS, libraries). Chunk Sidecars are configured to exactly replicate your CI pipeline environment. This eliminates the classic "it works on my machine" problem, which is especially common with AI-generated code.