Coding Agents Moved the Bottleneck to Review

The bottleneck moved

Coding agents have shifted the primary bottleneck in software development from code generation to code review and system architecture, according to Mihail Eric, adjunct professor at Stanford and Head of AI at Monaco. The floor for entry-level software engineering has risen significantly, he says, requiring junior engineers to possess skills comparable to senior levels. The role itself is evolving into a product-engineer hybrid focused on holistic system design and intent definition, because generating code is no longer the scarce skill. Knowing what should be built, and checking what an agent actually built, is. m:…4044

The same shift shows up inside a real engineering team. Packsize builds on-demand packaging systems that create right-sized corrugated boxes, and its engineering environment spans embedded machine controls, .NET services, Azure cloud infrastructure, roughly 50 microservices and React frontends. AI coding tools helped its engineers produce more code, faster. And then, in the words of its AI architect Nathan Hansen, the bottleneck moved from writing code to reviewing and verifying it. Volume went up; the constraint moved downstream. m:…8557

Adversarial review at Packsize

Packsize's answer was adversarial review. The team found that the AI that wrote the code was generally fine with its own implementation, so asking the same system to check its own work was not a check at all. It evaluated its options and chose Qodo as its dedicated code review and quality platform. Rather than relying on a single AI that might overlook its own mistakes, Qodo leverages multiple models to scrutinise code, and it automatically extracts review standards from the team's skill.md files in each repository and applies those rules during analysis. m:…8557

The review is not advisory. Qodo is a mandatory step in Packsize's software development lifecycle: no code goes to production without Qodo taking a look first. Its review flagged a critical compatibility fix that had to be implemented first, and without that safeguard the update would have caused a production outage. Qodo's native support for Azure DevOps meant it could plug directly into the team's existing pipelines, and code review time now takes just minutes. Packsize's next ambition is to move toward software factories, with cloud-based agents working more autonomously, and Qodo's Agentic Toolbox is part of that plan. m:…8557

Rebuilding the review interface

Greptile is rebuilding the other half of the problem: the interface humans review in. It has introduced a PR inbox to help teams work through reviews faster without sacrificing attention to detail. When a developer opens a pull request, merge status shows CI checks, merge conflicts, required approvals and the Greptile review in one place. A code tour tab groups related edits and explains what each change does, with key issues and surrounding code alongside it, and the developer can switch to the full diff to inspect individual edits. m:…7563

The inbox is built for a workflow where the reviewer talks back to the machine. A sidebar chat lets the developer select code and ask questions, or select a finding and ask why Greptile flagged it, using context from the review alongside the selection. If Greptile found an issue, the developer can send it straight to a coding agent to fix. Once a review is finished, the developer can approve or merge the pull request and move to the next with keyboard shortcuts, or select several pull requests to approve or merge together from the same inbox. m:…7563

Verification is the new skill

Eric's teaching follows the same logic. Stanford students are increasingly using coding agents rather than writing code by hand, and his course pushes them past simple prompting toward context engineering, agent-ready code base design and the orchestration of multi-agent systems. The practical advice is concrete: evaluate a code base for agent-readiness by improving documentation, verifiability and service-level boundaries, and treat dependency tracking and supply chain management as critical security mitigations, since the most secure code base is one with minimal dependencies. Monaco itself uses Devin for code review use cases after sampling multiple providers, and follows a buy to learn, build to keep philosophy for AI infrastructure. m:…4044

A preprint on the HEELO framework points at the same gap from the research side. It argues that existing agent approaches fail at the step from reaching a resource to applying it, which it calls the last mile to the real world. HEELO introduces agent-native primitives, reusable units that equip a resource with a resident language model that applies it in context, composes them into a task-specific graph, and verifies the result against evidence from the environment. In its tests, that verification loop reduced time and cost per completed task. The lesson is consistent across the classroom, the packaging company and the preprint: the scarce work is no longer producing code. It is proving the code is right. m:…0738