Anand Naidu is a seasoned development expert, fluent in both frontend and backend systems, who offers a sharp perspective on the tools that shape modern software engineering. We sat down with him to dissect a growing concern in the DevOps community: the hidden productivity tax of ubiquitous CI/CD platforms like GitHub Actions. Our conversation explored the deep-seated issues stemming from using YAML as a programming language, the frustratingly slow debugging cycles that plague developers, the broken promises of reusability, and the often-overlooked security and operational burdens that come with the seemingly convenient default choice.
Many CI/CD systems use YAML for configuration, forcing teams to express complex logic in a data format. What are the specific developer experience trade-offs of this approach, and can you share an anecdote where this brittleness led to significant, unexpected engineering costs?
The core problem, what some have called the “original sin” of modern CI/CD, is that we’re asking a data serialization format to act like a programming language. YAML was never designed for complex logic, branching, or sophisticated workflows. When you force it into that role, you lose all the safety nets and productivity boosters we take for granted in actual coding: type systems, testability, and clear, actionable error messages. Instead, our pipelines become these brittle, monstrous files that are terrifying to refactor. I remember one team that spent the better part of a week debugging a deployment failure. The root cause was a subtle indentation error in a deeply nested conditional step within their YAML file. The error message was completely opaque, leading them down a rabbit hole of checking cloud permissions and application code, all because the tool itself couldn’t provide a meaningful pointer to a simple syntax mistake. That’s a huge, demoralizing cost for something a real language linter would catch in milliseconds.
Engineers often face a slow, trial-and-error debugging cycle for pipeline failures. How does this “push-and-pray” loop impact team velocity and morale compared to local development? Could you walk through the tangible costs and context-switching overhead of this delayed feedback?
It’s absolutely brutal for both morale and velocity. Locally, a developer gets feedback in seconds. You change a line of code, the test fails, you know why, and you fix it. With this “push-and-pray” cycle in CI, every single attempt to fix a pipeline requires a commit, a push, and then a long wait—sometimes ten minutes or more—for the runner to provision, run all the preceding steps, and finally fail again. The tangible cost is immense; an engineer can lose an entire afternoon to what should be a five-minute fix. But the intangible cost is the context-switching. You push a change, and while you wait, you try to pick up another task. When the pipeline fails, you have to drop that new task, reload the mental model of the complex YAML file, and try to decipher cryptic logs. It’s one of the most dreaded and wasteful tasks in modern software engineering, and it slowly grinds a team’s momentum to a halt.
While most CI platforms offer features for reuse, teams still struggle to create truly DRY configurations. What specific limitations make robust abstraction so difficult in practice? Describe the technical debt that accumulates when a large organization cannot easily maintain consistency across hundreds of pipelines.
The promise of reuse is there, but the execution is deeply flawed. GitHub Actions offers reusable workflows and composite actions, but they come with a confusing and restrictive set of rules. For example, reusable workflows have significant limitations on how they can be composed together or how they pass inputs and outputs. Composite actions can’t even use certain features that are available in regular workflow steps. It creates this frustrating experience where you’re constantly fighting the platform’s limitations just to avoid copying and pasting code. For a large organization with hundreds of repos, this becomes a nightmare. You end up with dozens of slightly different, hardcoded pipeline variations. When you need to make a security update or change a deployment strategy, there’s no single source of truth to edit. It becomes a massive, error-prone manual effort, accumulating technical debt that nobody wants to own and everyone suffers from.
The marketplace of third-party actions is a major draw for its convenience. What specific supply chain security risks do teams inherit by using this ecosystem? Please detail the pros and cons of mitigation strategies like pinning actions to a specific commit SHA versus a version tag.
The marketplace is a double-edged sword. It offers incredible convenience, but every third-party action you use is essentially running unaudited code with access to your secrets, source code, and deployment credentials. The supply chain risk is very real; popular actions have been compromised in the past. Pinning an action to a version tag, like v1, seems safe, but that tag is mutable. The owner can move the v1 tag to point to a completely different, potentially malicious commit without you ever knowing. It provides only a thin veneer of security. A much safer strategy is pinning to a specific, immutable commit SHA. The pro is that you know exactly what code is running, every single time. The con is the maintenance burden; you have to manually track updates and update those SHAs across all your repositories. It forces a trade-off between security and maintenance overhead, and for organizations in regulated industries, it’s a significant governance challenge.
Teams often outgrow default CI/CD runners and must adopt self-hosted solutions. What operational burdens and hidden costs does this introduce, and how does this conflict with the initial promise of a managed service? Please share the key infrastructure concerns that often surprise engineering leaders.
This is a classic bait-and-switch scenario, though perhaps an unintentional one. Teams adopt a platform like GitHub Actions for the promise of a simple, managed service—to get out of the business of managing CI infrastructure. But as soon as your project grows, with larger test suites or specialized tooling, the default runners become too slow or inflexible. You’re then pushed towards self-hosting, and suddenly you’re back in the infrastructure game. Engineering leaders are often surprised by the total cost of ownership. It’s not just about spinning up a few virtual machines. You have to handle provisioning, security hardening, monitoring, patching, and scaling that fleet of runners. It’s a significant operational burden that directly conflicts with the initial reason you chose a “managed” service, and it’s a cost that rarely gets factored in during the initial evaluation.
The existence of alternatives like Dagger or Earthly suggests a demand for writing pipelines in a real programming language. What fundamental advantages does this “CI as code” approach offer over YAML-based systems, particularly regarding testability, composability, and IDE support?
The rise of tools like Dagger and Earthly is a direct response to the pain of YAML. The fundamental advantage of a true “CI as code” approach is that it brings all the power of mature software development ecosystems to your pipeline logic. With Dagger, for instance, you can write your pipeline in Go, Python, or TypeScript. This means you get proper type checking, which catches errors before you even run the code. You get full IDE support with autocompletion and inline documentation. Most importantly, you can actually unit test your pipeline logic locally, just like any other piece of software, which completely eliminates that painful “push-and-pray” debugging loop. The ability to create genuine, reusable functions and modules for your pipeline steps provides a level of composability and abstraction that YAML-based systems simply cannot dream of matching.
For organizations heavily invested in a platform like GitHub Actions, the switching costs can seem immense. What is a practical, incremental path for a team to begin mitigating these pain points without committing to a full-scale migration? What metrics would you track to prove the value?
A big-bang migration is usually a non-starter; the organizational inertia is just too strong. The practical path is incremental. You don’t have to rip and replace everything at once. A great starting point is to identify the most complex, brittle, and time-consuming job in your existing GitHub Actions workflow. You can then use a tool like Dagger to rewrite just that single piece of logic in a real programming language, while still using the GitHub Actions workflow to trigger it. This immediately gives you local testability and better logic for that painful step without disrupting the entire system. To prove its value, I’d track metrics like the number of failed CI runs for that specific job, the average time to resolve a failure (MTTR), and overall pipeline execution time. You could even survey developers on their satisfaction with the debugging experience. Seeing those numbers improve for one job makes a powerful case for expanding the approach.
What is your forecast for the future of CI/CD?
I believe we’re at an inflection point. The industry-wide experiment of using YAML as a quasi-programming language for CI/CD is revealing its limitations, and the costs are becoming too high to ignore. The future of CI/CD is a return to first principles of software engineering. We’ll see a definitive shift towards defining pipelines in general-purpose programming languages, moving this critical logic out of brittle configuration files and into testable, reusable, and maintainable code. This “CI as code” movement will become the standard, not the exception, because the productivity gains from local testing, IDE support, and robust abstraction are simply too significant. The tools that will win are the ones that empower developers to build and test their delivery pipelines with the same rigor and confidence they apply to their application code.
