real-world

The author, working at an AI infrastructure company, observes that running AI agents in production is less about the model and more about environment, access control, isolation, and safe state management, and asks if the community wants detailed architecture patterns.

An agency founder shares lessons from 50+ AI automation implementations, highlighting that most fail due to broken underlying processes, lack of internal ownership, and over-engineering, while the most successful automations are simple, focused, and backed by a named client-side owner.

Apex-Testing, a benchmark for evaluating agentic coding models using real private GitHub repositories, has been updated with recent models and detailed metrics including cost, time, and ELO-based leaderboard.

This paper introduces TerminalWorld, a benchmark for evaluating AI agents on real-world terminal tasks, derived from 80,870 terminal recordings. Current systems achieve at most 62.5% pass rate, highlighting challenges in authentic terminal workflows.

A reflection on the gap between impressive AI agent demos and dependable real-world execution, arguing that current agents excel at structured tasks but fail under unpredictable conditions, suggesting near-term AI roles will focus on narrow automation with human oversight.

A reflection on AI agents: impressive for narrow supervised tasks but fragile and unreliable in long-running, messy workflows due to issues like session expiration, context drift, and silent failures.

Anthropic's Agents team unveiled a production-grade four-layer framework for multi-agent systems during a 30-minute presentation, marking a shift from demo to real-world applications.

Mega-ASR proposes scaling up real-world acoustic simulation to improve automatic speech recognition in challenging, wild conditions, aiming to narrow the performance gap between lab and real-world settings.

DetectRL-X is a comprehensive multilingual benchmark for evaluating LLM-generated text detectors across 8 languages and 6 domains, including stress testing with AI-assisted writing operations and perturbations. It reveals strengths and limitations of current detectors in multilingual scenarios.