In this position paper published on arXiv (software engineering category), Martin Monperrus defends a thesis that runs head-on against a founding practice of the SDLC: coding agents have reached a level of capability such that human code review is no longer a necessary component of a quality pipeline. The argument rests on two claims. First, a parity — or even a superiority — of capability: autonomous LLM-based systems fulfill all the traditional goals of review (finding defects, improving quality, ensuring compliance, sharing knowledge) at lower cost and with higher throughput, without human fatigue or inconsistency. Second, a scaling problem: the dominant hybrid model — the agent writes the code, the human reviews it — provides neither real quality assurance nor the ability to keep up with AI-assisted production velocity; above all, it generates a false sense of security.
Monperrus situates his target historically, taking aim at inspection de Fagan (1976), and draws on the work of Bacchelli & Bird showing that review catches, in practice, fewer bugs than developers imagine. Benchmarks (SWE-bench, ~20-40% of issues resolved depending on the model, with rapid progression curves) serve as evidence of capability.
In place of human review, he proposes a multi-agent adversarial verification pipeline: an agent generates the code; one or more independent reviewer agents inspect it (defects, security, style); a verification layer adds automated tests and formal methods; a consensus mechanism has several agents vote to accept or reject. The bottleneck of a single human reviewer is replaced by distributed, tireless inspection.
The human does not disappear: they refocus on specification and high-level requirements, architectural trade-offs, oversight of critical domains, edge cases, and remain the final approval gate for sensitive systems. The author explicitly addresses the objections — hallucinations and prompt injection, the limits of automated testing (hence property-based testing), loss of domain expertise (offset by fine-tuning and RAG) — without dodging them.
On the SDLC side, he links review to DORA metrics: speeding up review throughput speeds up deployment. His recommendations are pragmatic: pilot first on low-risk components, keep an initial hybrid workflow (agents flag, humans approve), measure agent-versus-human detection rates, make rejection decisions explicit, and build feedback loops. A deliberately provocative text, but a valuable counter-thesis to the dogma of the "inviolable human review gate".