Qodo 2.1 Launches Rules System to Give AI Code Review Persistent Organizational Memory

Qodo 2.1 debuts a persistent governance layer designed to solve the short-term memory limits of today's AI code assistants. The release embeds an organizational memory of standards directly into review agents, and Qodo reports a 11% improvement in precision and recall while flagging 580 defects when validating the system on 100 production pull requests.

The product removes the need for scattered, hand-maintained rule files by automatically extracting standards from repositories and historical review feedback. Rules are proposed to technical leads for approval, then enforced at pull-request time with concrete fix suggestions. Qodo adds continuous rule maintenance to resolve duplicates, surface conflicts, and retire stale standards to avoid rule drift. The company pairs this rule fabric tightly with its agents, rather than treating memory as a separate external store, to reduce search overhead and increase contextual accuracy. Qodo also applies fine-tuning and reinforcement approaches to align agent behavior with approved rules, which the vendor pins as the source of the precision gains. The system ships with enterprise deployment choices: on-prem/cloud-premise, single-tenant hosted instances, and standard SaaS. Pricing remains seat-based: a free Developer tier offers 30 PR reviews/month, the Teams tier lists at $38 per user/month including 20 PRs per user/month and 2,500 IDE/CLI credits, and larger customers negotiate a custom Enterprise plan. Early adopters report faster onboarding and more consistent reviews after operationalizing scattered policies into a single executable source of truth. Qodo positions this pattern as a blueprint for making AI-driven development tools truly stateful and enterprise-ready by 2026.