Traditional Code Review Is Dead. What Comes Next?

I noticed a quiet shift in our engineering team recently that brought me to a broader realization about the future of software development: Code review has changed fundamentally.

It started with a pull request (PR). An engineer had used an agent to generate the entire change, iterating with it to define business logic, but ultimately relying on the agent to write the code. It was a substantial chunk of work. The code was syntactically perfect. It followed our linting rules. It even included unit tests that passed green.

The human reviewer, a senior engineer who is usually meticulous about architectural patterns and naming conventions, approved it almost immediately. The time between the PR opening and the approval was less than two minutes.

When I asked about the speed of the approval, they said they checked if the output was correct and moved on. They did not feel the need to parse every line of syntax because it was written by an agent. They spun up the deploy preview, clicked the buttons, verified the state changes and merged it.

This made sense, but it still took me by surprise. I realized that I was witnessing the silent death of traditional code review.

The Silent Death of the Code Review

For decades, the peer review process has been the primary quality gate in software engineering. Humans reading code written by other humans served two critical purposes:

• It caught logic bugs that automated tests missed.
• It maintained a shared mental model of the codebase across the team.

The assumption behind this process was that code is a scarce resource produced slowly. A human developer might write 50 to 100 lines of meaningful code in a day. Another human can reasonably review that volume while maintaining high cognitive focus.

But we are entering an era where code is becoming abundant and cheap. In fact, the precise goal of implementing coding agents is to generate code at a velocity and volume that by design makes it impossible for humans to keep up.

When an engineer sees a massive block of AI-generated code, the instinct is to offload the syntax-checking to the machine. If the linter is happy and the tests pass, the human assumes the code is valid. The rigorous line-by-line inspection vanishes.

The Problem: AI Trust and the Rubber Stamp

This shift leads to what I call the rubber stamp effect. We see a “lgtm” (looks good to me) approval on code that nobody actually read.

This creates a significant change to the risk profile. Human errors usually manifest as syntax errors or obvious logic gaps. AI errors are different. Large language models (LLMs) often hallucinate plausible but functionally incorrect code.

Traditional diff-based review tools are ill-equipped for this. A diff shows you what changed in the text file. It does not show you the emergent behavior of that change. When a human writes code, the diff is a representation of their intent. When an AI writes code, the diff is just a large volume of tokens that may or may not align with the prompt.

We are moving from a syntax-first culture to an outcome-first culture. The question is no longer “Did you write this correctly?” The question is “Does this do what we asked the agent for?”

Previews as the New Source of Truth

In this new world, where engineers are logic architects who offload the writing of code to agents, the most important artifact is not the code. It is the preview.

If we cannot rely on humans to read the code, we must rely on humans to verify the behavior. But to verify behavior, we need more than a diff. We need a destination. The code must be deployed to a live environment where it can be exercised.

While frontend previews have become standard, the critical gap — and the harder problem to solve — is the backend.

Consider a change to a payment processing microservice generated by an agent. The code might look syntactically correct. The logic flow seems correct. But does it handle the race condition when two requests hit the API simultaneously? Does the new database migration lock a critical table for too long?

You cannot see these problems in a text diff. You cannot even see them in a unit test mock. You can only see them when the code is running in a live, integrated environment.

A backend preview environment allows for true end-to-end verification. It allows a reviewer to execute real API calls against a real database instance. It transforms the review process from a passive reading exercise into an active verification session. We are not just checking whether the code compiles. We are checking whether the system behaves.

As AI agents write more code, the “review” phase of the software development life cycle must evolve into a “validation” phase. We are not reviewing the recipe. We are tasting the dish.

The Infrastructure Challenge: The Concurrency Explosion

However, this shift to outcome-based verification comes with a massive infrastructure challenge that most platform engineering teams are not ready for.

A human developer typically works linearly. They open a branch, write code, open a pull request, wait for review and merge. They might context switch between two tasks, but rarely more.

AI agents work in parallel. An agent tasked with fixing a bug might spin up 10 different strategies to solve it. It could open 10 parallel pull requests, each with a different implementation, and ask the human to select the best one.

This creates an explosion of concurrency.

Traditional CI/CD pipelines are built for linear human workflows. They assume a limited number of concurrent builds. If your AI agent opens 20 parallel sessions to test different hypotheses, you face two prohibitive problems: cost and contention.

First, you cannot have 20 full-scale staging environments spinning up on expensive cloud instances. Imagine spinning up a dedicated Kubernetes cluster and database for 20 variations of a single bug fix. The cloud costs would be astronomical.

Second, and perhaps worse, is the bottleneck of shared resources. Many pipelines rely on a single staging environment or limited testing slots. To avoid data collisions, these systems force PRs into a queue.

With existing human engineering teams, these queues are already a frustrating bottleneck. With multiple agents dumping 20 PRs into the pipe simultaneously, the queue becomes a deadlock. The alternative of running them all at once on shared infrastructure results in race conditions and flaky tests.

Scaling Development With Environment Virtualization

To scale agent-driven development, we cannot rely on infrastructure built for linear human pacing. We are talking about potentially hundreds of concurrent agents generating PRs in parallel, all of which need to be validated with previews. Cloning the entire stack for each one is not a viable option.

The solution is to multiplex these environments on shared infrastructure. Just as a single physical computer can host multiple virtual machines (VMs), a single Kubernetes cluster can multiplex thousands of lightweight, ephemeral environments.

By applying smart isolation techniques at the application layer, we can provide strict separation for each agent’s work without duplicating the underlying infrastructure. This allows us to spin up a dedicated sandbox for every change, ensuring agents can work in parallel and validate code end-to-end without stepping on each other’s toes or exploding cloud costs.

Conclusion

There is a clear shift happening in the way we review changes. As agents take over the writing of code, the review process naturally evolves from checking syntax to verifying behavior. The preview is no longer just a convenience. It is the only scalable way to validate the work that agents produce.

At Signadot, we are building for this future. We provide the orchestration layer that enables fleets of agents to work in parallel, generating and validating code end-to-end in a closed loop with instant, cost-effective previews.

The winners of the next era won’t be the teams with the best style guides, but those who can handle the parallelism of AI agents without exploding their cloud budgets or bringing their CI/CD pipelines to a grinding halt.

In an AI-first world, reading code is a luxury we can no longer afford. Verification is the new standard. If you cannot preview it, you cannot ship it.

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