Explainer Collection

AI Engineering

Lin et al. (2026)

An agent rewrites its own coding harness, and beats the engineers who used to do it by hand

Agentic Harness Engineering turns harness tuning into an automated loop. Ten iterations lift pass@1 on Terminal-Bench 2 from a bash-only 69.7% to 77.0%, past the human-built Codex harness and every self-evolving baseline.

77.0%: pass@1 on Terminal-Bench 2 after ten automated iterations, up from a 69.7% bash-only seed and above the human-designed Codex harness at 71.9%
+10.1 pp: largest cross-family gain when the frozen harness is dropped onto deepseek-v4-flash, with no re-evolution at all

Demirer, Musolff & Yang (2026)

AI coding agents triple the code developers write, but shipped software barely budges

A study of more than 100,000 GitHub developers finds that each generation of AI coding tool delivers bigger task-level gains, yet those gains shrink dramatically as they travel down the production chain toward actual releases and end users.

180%: cumulative increase in weekly commits after adopting all three generations of AI coding tools
30%: what that 180% gain shrinks to by the time it reaches actual software releases

Liu (2026)

AI coding assistants fix more code smells than they create, but introduce nearly twice the security issues they resolve

Across 304,362 AI-authored commits from 6,275 GitHub repositories, AI tools are a net positive for surface-level code quality but a net negative for bugs and security vulnerabilities, with 24.2% of all introduced issues persisting indefinitely.

484,606: distinct quality issues identified across 304,362 AI-authored commits from 6,275 GitHub repositories
89.1%: of all AI-introduced issues are code smells, the dominant but least dangerous form of technical debt

Li (2025)

AI coding agents now ship 456,000 pull requests, but their code gets rejected far more often than human work

The first large-scale dataset of autonomous coding agent activity on GitHub reveals that speed and scale are real, but acceptance rates, review dynamics, and code complexity tell a more sobering story about the gap between benchmarks and production.

456K: pull requests authored by five autonomous coding agents across 61,000 repositories and 47,000 developers
35–65%: agent PR acceptance rates, compared to 77% for human developers, a 12–42 percentage point gap

Humberd (2026)

Agency theory was built for human managers, but AI is becoming the agent nobody knows how to supervise

A new framework maps five stages of AI evolution against traditional agency mechanisms, arguing that firms need to scaffold monitoring and incentive systems now, well before AI gains full decision-making autonomy.

Routine AI: mimics rote human decisions and stays fully under human control, like a reorder system that restocks at a fixed threshold
Machine AI: adapts human-set algorithms by pulling in structured external data such as sales forecasts to improve decision inputs