Code review has been one of software engineering’s central quality gates for decades. Michael Fagan’s 1976 IBM paper formalized a structured inspection process with defined roles, preparation, review, rework, and follow-up. Yet modern pull requests preserve the same core idea: another perspective should examine a change before it becomes part of the system.
In 2026, that assumption received a serious challenge. A KTH Royal Institute of Technology position paper argued that sufficiently capable coding agents can make traditional human inspection economically unnecessary in some settings. The claim is deliberately provocative. It is also useful because it forces engineering leaders to ask what AI code review should protect when a meaningful share of the code was not typed by a person.
The answer is not less review. Instead, the review boundary must move. As a result, automated systems can absorb more mechanical checking while people concentrate on intent, architecture, operational risk, and accountability.
The argument for agent-led review is not baseless. For example, modern coding agents can inspect repositories, plan changes, edit several files, run tests, interpret failures, and revise their work. In addition, review agents can summarize pull requests, enforce style rules, identify known vulnerability patterns, and search for suspicious changes in parallel.
However, the KTH paper is a position paper supported by an economic model, not proof that human review has become obsolete across production software. Its conclusion depends on the agent’s defect-detection recall, the cost of human inspection, and the consequences of missed defects. Therefore, the result can vary sharply between a low-risk internal tool and a payment, healthcare, or identity system.
More importantly, mechanical correctness is only one layer of review. A patch can compile, pass its tests, and still solve the wrong problem. It can also violate an architectural direction, weaken an authorization boundary, create an expensive operational dependency, or make the next change harder.
Research published in 2026 found that LLM reviewers can systematically over-correct when judging whether code conforms to natural-language requirements. That limitation matters because requirements are rarely complete enough to encode every business constraint. Consequently, human reviewers still supply context that does not live in the diff.
Textbook-correct, locally wrong. An agent may implement a familiar pattern accurately while ignoring the codebase’s conventions, domain language, error model, or ownership boundaries. Each isolated decision looks reasonable; the system becomes less coherent when the decisions accumulate.
Reinvention across sessions. Separate agent sessions can solve the same category of problem in different ways when shared context is weak. The result is duplicated helpers, competing abstractions, and several partial versions of the same policy.
Popularity-driven dependencies. Agents may recommend libraries that appear frequently in their training data without considering the project’s approved dependency set, security posture, maintenance policy, or existing platform capabilities. Consequently, a small convenience can quietly expand the attack surface and support burden.
Confident, hollow documentation. Generated comments often describe what the code does while omitting why the design exists, which trade-off was accepted, and what must remain true. However, the next engineer still lacks the information needed to change it safely.
Test-shaped correctness. An agent can optimize toward visible tests and produce a patch that satisfies the suite without covering the real operating conditions. Reviewers should look for missing failure modes, weak assertions, over-mocking, and tests that merely repeat the implementation.
Architectural erosion in small increments. A 2026 study of long-horizon agent work found rising verbosity and structural degradation as agents repeatedly extended their own code. A passing test suite therefore does not guarantee that a codebase remains understandable or easy to evolve.
Strong teams divide review into layers. Static analysis, formatting, dependency scanning, policy checks, test execution, and an independent AI review can form the first pass. These controls should produce evidence, not an automatic presumption that the patch is safe.
Next, the human reviewer examines the change at system level. Does it match the requested behavior? Does it reuse the right abstractions? Are trust boundaries preserved? Is the migration reversible? Will telemetry expose failure? Who owns the result after deployment? These questions require knowledge of the product and organization, not only the repository.
Context engineering should begin before generation. Give agents explicit architecture rules, approved libraries, domain terminology, testing expectations, security constraints, and examples of preferred patterns. This resembles onboarding a contributor more than writing a clever one-line prompt.
Provenance also matters. Pull requests should identify which agent and model produced the change, what instructions and tools it received, which tests it ran, and where a human intervened. As a result, reviewers can calibrate attention and incident responders gain a clearer chain of evidence.
Finally, separate generation from verification. A reviewer—human or automated—should challenge the patch with independent tests and assumptions. Asking the same system to approve the reasoning it just produced can preserve the same blind spots.
A conventional diff answers what changed. Agent-era review must also ask why the agent chose that approach and what pattern the repository will learn from it. Because future sessions retrieve and imitate existing code, one tolerated shortcut can become a template for many later changes.
That makes refactoring more urgent. Inconsistent abstractions, obsolete helpers, and misleading examples should be removed before agents copy them. Teams should also maintain short, versioned engineering guidance close to the code and test whether agents actually follow it.
Review metrics need adjustment as well. Faster merge time is not enough. Track rollback rates, escaped defects, dependency growth, duplicate abstractions, review rework, security findings, and maintainability over repeated agent contributions. The objective is not maximum code volume. It is dependable change at a sustainable cost.
Code review in 2027 will look different from code review in 2020. It will be faster at the mechanical layer and less focused on formatting, boilerplate, and obvious mistakes. Yet its original purpose—finding risk and weak judgment before release—remains.
As agents generate more of the implementation, human attention becomes scarcer and more valuable. The responsible response is to spend that attention where automation is weakest: ambiguous intent, architecture, cross-system effects, security trade-offs, and business consequences.
Teams that treat autonomous coding as permission to remove scrutiny are likely to ship defects faster. Therefore, teams that redesign review around agent-specific failure modes can capture the speed benefit without surrendering engineering judgment.
For a related look at how AI-generated code can compound maintainability risk, read Tech Debt at Machine Speed: Managing the Hidden Cost of AI-Generated Code.
Healimpilo is a technology advisory firm working with companies across healthcare, fintech, life sciences, and SaaS on engineering strategy, talent, and delivery.
