State of Ai Engineering

Governing Ai Engineering

— Sri Rang, Solutions Architect @ Qodo
Author, Platform Agentic "Definitive Guide for Building Compliant Ai Agents"

References 1/2

References 2/2

Facts

Fact 01 — Universal AI Coding Adoption

AI Code-Generation Adoption
74%
AI-Generated Code
41%
Fortune 100 companies using AI
90%
Code created per dev vs. 2022
+75%


* Source: GitClear — Worldwide — As of Jan 2026

Diagnostics

  • Which AI coding tools are your devs using — sanctioned or otherwise?
  • What percentage of your code is AI generated?
  • How has PR volume per developer changed in the last 12–18 months?
  • Are some teams further along than others?
    • Which ones moved first, and why?

Fact 02 — Code Review Bottleneck

For teams with high AI adoption

Reporting More PR's Merged
+98%
Reporting Longer PR Reviews
+91%
  • PR sizes up 154%
  • Discovered bugs up 9%
  • DORA delivery metrics unchanged

44% of teams name
slow code reviews
as their
single biggest delivery bottleneck

"More code, fewer releases" — Waydev's named blind spot of 2026 aka. "AI Productivity Paradox" — Faros AI, 10,000+ devs

Diagnostics

  • Typical PR-cycle at your Org. — from open to merge.
    • Where do PRs get stuck the longest?
    • What's your average time-to-first-review? Time-to-merge?
  • How many reviewers does a typical PR need?
    • How often does it need a senior/staff/principal dev?
    • What % of your engineering time go into review vs. building?
  • Has PR size grown in the last 12-18 months?
    • Have your DORA metrics improved since AI adoption?

If a PR sits 2 days waiting on review, what does that cost you in throughput?

Fact 03 — Quality & Security Risk

Vulnerability Density
2.74×
Ai generated code vs. Human generated code
AI Code Failing Security Tests
45% overall
Java = 72%
Python / C# / JS = 38–45%
Security findings per month
10,000+
10× jump, Dec 2024 → Jun 2025
Refactoring Rate
25% → <10%
Code Duplication = 4×
GitClear, over 211M lines

Diagnostics

  • Have you seen incidents or near-misses traced back to AI-generated code?
  • How do you catch security issues today — pre-merge, post-merge, or both?
    • What's your bug escape rate look like — pre-AI vs. now?
    • How much time does your AppSec team spend on findings?
    • Has rework or revert volume changed in the 12-18 months?
  • Is anyone tracking duplication or refactoring discipline?

When AI-generated code introduces vulnerabilities, who catches it — and how late in the cycle?

Fact 04 — Engineering Leaders Budget

Current Spend
per dev/year
$101–500

Reported by 38.4% of leaders
~50% allocate 1–3% of total budget

Emerging 2026 target
per dev/year
$1,000

85.7% reserving budget for
tools beyond code generation

  • 15–20% of AI tooling budget earmarked for adjacent use cases
    • Code Review, Governance, Security, Planning
  • 86% of leaders uncertain which AI tools deliver the most ROI

Diagnostics

  • How are you thinking about AI tooling spend in 2026 vs. 2025?
    • Beyond code authoring, what other use cases are you exploring?
  • What's your per-dev annual spend on AI tools today?
  • How are you measuring ROI on the AI tools you've already deployed?

Qodo Architecture

sequenceDiagram box actor Developer participant Git.Platform as Git Platform participant Planning.Tool as JIRA / Linear / AzDO end box Purple participant Qodo.Code.Review as Code Review participant Qodo.Rules.Engine@{ "type" : "collections" } as Rules Engine participant Qodo.Context.Engine@{ "type" : "collections" } as Context Engine end Developer->>Git.Platform: Commits feature branch Developer->>Git.Platform: Creates new PR Git.Platform->>Qodo.Code.Review: PR ready-for-review Qodo.Code.Review-->>Git.Platform: Fetch code Note over Git.Platform,Qodo.Code.Review: Shallow clone of the feature branch. Qodo.Code.Review-->>Planning.Tool: Fetch issue/ticket for this feature. Note over Planning.Tool,Qodo.Code.Review: Extract acceptance criteria from specifications Qodo.Code.Review-->>Qodo.Rules.Engine: Fetch rules and review-guidelines Note over Qodo.Rules.Engine,Qodo.Code.Review: Pulls team, project, org defined review rules Qodo.Code.Review-->>Qodo.Context.Engine: Fetch additional context Note over Qodo.Context.Engine,Qodo.Code.Review: Additional context from related projects and PR history loop Qodo.Context.Engine-->Git.Platform: Continuous indexing of repos and PRs - background process end Qodo.Code.Review-->>Git.Platform: Action Required, Review Recommended Note over Qodo.Code.Review,Git.Platform: Publishes review as PR comment Git.Platform-->>Developer: Review available notification

Deployment Options

  1. Multi-tenant SaaS
    • Hosted in 🇺🇸
  2. Dedicated, single-tenant SaaS
    • Hosted in 🇪🇺, or any region of your choice
  3. On-Prem / Cloud-Prem
    • Hosted on your Kubernetes cluster
    • Optionally, Bring-Your-Own-Keys
  4. Air-Gapped
    • Your GPUs and Data-Center

Benchmarks

Open-source, Peer-reviewed

Code Review Benchmarks

Benchmarks — Overview

  • Largest open-source, code-review benchmarks
    • Transparent, peer-reviewed inputs & methodology
  • Conducted from mid-Jan to early-Feb 2026
    • All tools had the latest models
    • All tools had default configurations
    • Zero methodology bias

Benchmarks — DataSet & Contestants

  • Top open-source repos
  • 100 real, merged PRs
  • 580 human-verified issues
  • cal.com TypeScript
  • Ghost JavaScript
  • dify Python, Go
  • firefox-ios Swift
  • prefect Python
  • tauri Rust
  • aspnetcore C#
  • redis C
  • Qodo
  • Augment
  • Copilot
  • Cursor
  • Greptile
  • Codex
  • Coderabbit
  • Sentry

Benchmarks — Yardstick

  • Precision "When I flag something, am I right?"
  • Recall "Did I catch everything?"
  • F1 Score Harmonic mean of Precision & Recall

Benchmarks — Results

github.com/agentic-review-benchmarks/benchmark-pr-mapping

Agent Precision (%) Recall (%) F1 (%)
Qodo - Exhaustive 63.8 56.7 60.1
Qodo - Precise 74.5 44.2 55.4
Augment 70.6 32.1 44.1
Copilot 50.1 37.4 42.8
Cursor 78.5 26.2 39.3
Greptile 68.5 27.2 39.0
Codex 83.0 24.3 37.6
Coderabbit 53.7 19.0 28.0
Sentry 85.3 13.8 23.7

Build vs. Buy

Build Options

  • Build Option 1 — Platform team builds and maintains centralized review agent for entire Org
  • Build Option 2 — Each team builds and maintains their own, custom review agent

 

  • Qodo — Benchmark-proven, Enterprise ready, SOTA review agent

Core Review Capabilities

Dimension Build: Central Review Agent Build: Custom per Team Buy: Qodo
Precision & Recall Unknown Unknown, Inconsistent Benchmark-proven, SOTA
Context Engine Must build Doesn't exist Benchmark-proven, SOTA
Rules Engine Must build Doesn't exist Included
Temporal learning (PR history) Doesn't exist Doesn't exist Included

Operations & Governance

Dimension Build: Central Review Agent Build: Custom per Team Buy: Qodo
Metrics Separate system to build Doesn't exist Included
LLM cost monitoring Separate system to build Doesn't exist Included
Enterprise plumbing (SOC 2, SSO etc.) Must build Multiplied risk per team Included
Engg. leader visibility Eventually Zero Day one

Risk

Dimension Build: Central Review Agent Build: Custom per Team Buy: Qodo
Maintenance burden You, centralized You, fragmented Qodo
Opportunity cost Engineers off product roadmap Engineers off product roadmap Focus on product roadmap
Failure mode Slow, political, single point of failure Fragmented, duplicative, inconsistent Qodo

Cost

Dimension Build: Central Review Agent Build: Custom per Team Buy: Qodo
LLM API spend High — but at least centralized Multiplies across teams Included; lower per-PR

Speed and Coverage

Dimension Build: Central Review Agent Build: Custom per Team Buy: Qodo
Time to value Months Weeks, never org-wide Days
Org-wide consistency High None High
Project-level customization Slow — gated by central team Native Custom rules