Building quality into the agentic workflow

AI coding agents have increased development speed but also created a trust gap, as teams often must manually re-review agent-generated code, reducing productivity gains. A Qodo live session argued that code review should move earlier into the coding session rather than begin at the pull request, using organizational context such as cross-repository dependencies, engineering rules, and prior PR discussions to identify issues before changes are finalized. Qodo’s Agentic Toolbox integrates full AI review capabilities into coding agents, allowing developers to run iterative, detailed local reviews while their work and context remain active, including analysis across repositories not stored locally. The approach distinguishes between comprehensive findings for agents, which can handle high volumes of feedback, and lower-noise reviews for human PR reviewers. Under this model, developers can address both blockers and broader quality improvements before opening a PR, leaving pull requests as a final gate for prioritizing human attention, approving low-risk changes, and improving the system through feedback on accepted or dismissed findings.

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