Agentic Coding Statistics

---
title: Agentic Coding Statistics & Failure Rates 2026
source_url: https://mikemason.ca/writing/ai-coding-agents-jan-2026/
category: trends
tags: statistics, adoption, failure-rates, quality, enterprise
relevant_agents: checker, gal, planner, orca, all
fetched_date: 2026-02-13
last_updated: 2026-02-13
content_type: research
difficulty: intermediate
description: Hard data on AI coding agent adoption, failure rates, and quality metrics from Mike Mason and industry sources
keywords: statistics, adoption, failure rates, bug rates, code quality, enterprise
copyright_notice: "Content gathered under fair use for research purposes. See Legal agent for IP questions."
---

# Agentic Coding Statistics & Failure Rates 2026

## Executive Summary

Hard data on AI coding agent adoption and failure rates. **Key finding:** High adoption correlates with increased bug rates and code review time.

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## Adoption Statistics

| Metric | Value | Source |
|--------|-------|--------|
| Companies running AI agents in production | **57%** | Industry survey |
| AI integration in developer work | **60%** | Anthropic |
| Claude Code written by Claude Code (at Anthropic) | **~90%** | Steve Yegge |

### Productivity Claims

| Developer | Claim | Note |
|-----------|-------|------|
| Steve Yegge | ~12,000 lines/day | Built 225,000+ lines of Go (Beads) in 6 days |

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## Quality & Failure Metrics

### Google's 2025 DORA Report

| Metric | Change |
|--------|--------|
| Bug rates | **+9%** (with 90% AI adoption increase) |
| Code review time | **+91%** |
| PR size | **+154%** |

### AI-Generated Code Rejection

| Code Type | Rejection Rate |
|-----------|---------------|
| AI-generated PRs | **67.3%** rejected |
| Manual code PRs | 15.6% rejected |

### Code Churn & Quality

| Metric | Change |
|--------|--------|
| Code churn | **Doubled** (2021-2023) |
| Refactoring | Dropped from 25% to under 10% |
| Copy/paste code | Increased from 8.3% to 12.3% |
| Duplicated code blocks | **8-fold increase** |

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## Perception Gap

> "Experienced maintainers were **19% slower** with AI tools while believing they were 20% faster."

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## Scale Limitations

### Enterprise Capability Levels

| Task Type | AI Capability |
|-----------|--------------|
| Multi-file refactors (enterprise) | **42%** |
| Legacy codebases | **35%** |
| Files larger than 500KB | Often excluded entirely |

### Expert Assessment

> "I haven't seen [autonomous agents] actually work a single time yet." — Industry observer

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## Multi-Agent Orchestration Patterns

### Cursor's Hierarchical Model