The code review is dead... long live the code review

The code review has been a fundamental part of the practice of software engineering for decades. However, as AI writes increasingly more of our code, we need to entertain the possibility that its time may have passed. In 2026, the traditional asynchronous pull request (PR) review, the gatekeeper of the modern delivery pipeline, is breaking.

But if the code review is broken, possibly dead, what takes its place? As many of us in the industry have been reflecting on in recent months, the rigor needs to go somewhere.

The bottleneck of sudden abundance

For the last fifteen years, the pull request has been the industry’s default mechanism for quality control, mentorship and knowledge sharing. A developer writes code, packages it up and asks a peer to look it over before it merges into the main branch. When humans write code line-by-line, this workflow functions, albeit with well-documented friction. However, we’re no longer in a human-only coding environment.

With the rise of generative AI and agentic coding tools, the volume of code production has skyrocketed. An engineer can prompt an LLM to generate entire feature blocks, refactor legacy modules or spin up boilerplate services in a fraction of the time it takes to type them out.

We’re facing a crisis of the modern PR queue. If an AI agent can generate 500 lines of code in five seconds, but a human engineer still requires thirty minutes of deep cognitive focus to thoroughly review those same 500 lines, the review queue becomes a catastrophic bottleneck.

When faced with an overwhelming flood of code, human behavior adapts predictably but destructively. Reviews degrade into superficial rubber-stamping or pedantic nitpicking over style choices, the ultimate form of bike-shedding. The asynchronous wait times compound, killing the momentum of continuous integration and ballooning a team's work-in-progress (WIP). The mechanical act of a human line-by-line diff audit cannot survive the era of AI-generated software. In that sense, the traditional code review is dead.

The human core: mentorship and collaboration in a post-PR world

It’s important to acknowledge that for a generation of engineers, the PR description and its comment threads have been the primary vehicle for knowledge sharing and mentorship. 

There’s a risk that losing the PR queue will deepen engineering silos and create a generation of developers who can prompt, but cannot critically evaluate system design. To prevent this, our approach to collaboration must evolve from asynchronous correction to synchronous co-creation.

Mentorship of intent, not syntax

In the past, a significant portion of code review feedback was syntactical or tactical: "Use a map here instead of a loop," or "we have a utility function for this." AI agents are already exceptional at this level of remediation, so mentorship needs to shift upward to the level of intent, architecture and business domain alignment.

Seniors must guide juniors in framing problems, evaluating trade-offs between competing architectural patterns and questioning the underlying assumptions of the product requirements. We are no longer teaching apprentices how to swing the hammer; we are teaching them how to read the blue-prints and understand the soil mechanics.

From asynchronous review to a return to mobbing

Asynchronous code reviews have always been an inefficient way to collaborate. In an AI-native world, this latency is untenable. We need, then, to leverage established synchronous practices like pair or mob programming.

Pairing changes flavor when an AI is in the room. It becomes a triad: a senior engineer, a junior engineer and an AI agent. The senior engineer models the critical thinking, the junior guides the execution and the AI accelerates the generation. This real-time collaboration ensures that knowledge is shared while the system is being shaped, rather than weeks later in a frantic rush to meet a sprint deadline.

Elevating collective ownership

When code is cheap to produce, it is easy to lose a sense of collective ownership. If you didn’t write or review it, is it really your codebase?

To maintain a healthy, shared mental model of our software, teams need to institutionalize new forms of alignment: this includes replacing stale documentation with lightweight, (perhaps AI-assisted) summaries of systemic changes and periodically reviewing parts of the codebase generated by agents as a team, not to find bugs, but to ensure everyone comprehends the system's current topology.

Designing constraints

If we accept we can no longer rely on a post-hoc manual review to catch flaws in an avalanche of code, we need to ask ourselves how we can still ensure quality. 

The answer is that we require a shift from gatekeeping to supervisory engineering and constraint design.

To make code reviews live on in a meaningful way, technologists need to redirect their expertise toward four distinct areas:

Reframing TDD

TDD shouldn’t just be 'humans writing failing tests' — in the context of AI-assistance this isn’t that effective. We need to reframe TDD as writing the test first as the ultimate executable spec for the AI. Even though we’re moving away from manual 'ping-pong' coding with AI and moving toward autonomous agents, we can still embed the TDD philosophy directly into the agent’s system prompt or role.

Moving from specifications to constraints

Rather than trying to tell an agent exactly how to write code, senior engineers must learn to design the boundaries within which the agent can safely operate. This means shifting rigor into robust type systems, architectural guardrails and automated security policies that make incorrect behavior unrepresentable. When a constraint is violated, it triggers an intentional human architectural intervention, rather than an arbitrary PR comment.

Continuous comprehension over line-by-line diffs

If code changes faster than a human can read it, our mental models of the system decay. To counter this, teams need to embrace continuous comprehension. This involves replacing asynchronous diff-reading with synchronous practices like ensemble (or mob) programming for complex design areas, regular architecture retrospectives and using AI tools to synthesize high-level system changes on demand. 

Think lean

Although AI can generate massive amounts of code, the value we can bring as human developers is keeping things lean. That means prioritizing short commits and code releases, validating our hypotheses and launching small features before upgrading them. In other words, we need to keep it simple, an important tenet of good software craftsmanship.

It’s important to emphasize that supervisory engineering isn’t about isolated humans overseeing agents. This work should undoubtedly be collaborative — when we talk, for instance, about the ‘mentorship of intent’ what we are really talking about is cultivating the knowledge and understanding of context to effectively orchestrate and supervise a sophisticated agentic system. That knowledge, after all, can’t live inside one person’s mind and it probably should be forced into an agents.md file…

Long live the code review!

The core objectives of the code review have always been noble: distributed ownership of the codebase, collective learning, mentorship and ensuring technical excellence. None of those goals have changed. What has is that we can no longer use a manual, slow-moving bureaucratic process to achieve them.

The future belongs to technology leaders who recognize that code production is cheap, but comprehension and risk management are expensive. We need to stop treating code reviews as a mechanism for catching syntax errors and start treating them as strategic checkpoints for human alignment, architectural integrity and system safety.

The mechanics of the PR are fading away, but the spirit of the code review — the relentless pursuit of shared understanding and technical rigor — must endure.