The article catalogs dozens of AI agent failure modes—from one‑shot attempts and cold‑start amnesia to hidden harness control—and explains why these issues quickly overwhelm developers, then outlines concrete mitigation strategies and their trade‑offs.
The article dissects Codex’s /goal feature, showing how it adds persistent goal objects, a runtime lifecycle, completion auditing and budget handling, turning long‑running agents from a simple repeat‑loop into a robust, state‑driven engineering workflow.
The article analyzes how AI coding tools boost delivery speed yet erode maintainability, explaining that the root cause is the mismatch between traditional deterministic software engineering and the inherent non‑determinism of large language models, and proposes a structured engineering framework to mitigate the risks.
The author reports a personal experiment where adding a concise CLAUDE.md file to 30 repositories reduced Claude Code's error rate from 41% to 3%, explains why such a tiny contract influences agent behavior, expands the original four Karpathy rules into twelve practical guidelines, and offers concrete advice on writing, structuring, and maintaining effective CLAUDE.md files.
Martin Fowler argues that the biggest shift in AI‑driven software development is no longer about making models write code, but about integrating the inherent non‑determinism of AI into a verifiable, rollback‑capable engineering pipeline—what he calls Harness engineering—to preserve reliability and governance.
In a December 2025 interview, Andrej Karpathy explains how Vibe Coding lowered the software‑creation barrier, why Agentic Engineering shifts responsibility from models to humans, and what engineers must master to manage AI agents safely and effectively.
In a detailed interview, Andrej Karpathy traces the evolution of AI‑assisted software development, contrasting early Vibe Coding with the emerging Agentic Engineering paradigm, explains Software 3.0’s workflow, highlights the limits of current LLMs, and outlines future opportunities for AI‑native engineers.
Karpathy argues that AI agents are reshaping software development by turning the LLM context window into a programmable layer, redefining the basic unit of work, and introducing a verifiability‑driven framework that separates domains where models excel from those where they still stumble.
In a 2026 Sequoia Ascent interview, Andrej Karpathy argues that large language models are not merely speed‑up tools but a new computing paradigm that renders many legacy apps obsolete, elevates understanding as humanity’s final competitive edge, and relegates CPUs to auxiliary roles, while outlining software evolution, jagged intelligence, and the rise of agentic engineering.
Karpathy’s 2026 Sequoia AI Ascent interview shows that while Vibe Coding lowers the barrier for rapid prototyping, the emerging Agentic Engineering paradigm pushes AI agents into the full software‑development lifecycle, demanding new control planes, verification, context handling and blurring the line between senior engineers and architects.
In a Sequoia Ascent interview, Andrej Karpathy argues that large language models are reshaping software into a new computing paradigm, making many existing apps obsolete, emphasizing verifiable tasks as the remaining human moat, and predicting CPUs will become auxiliary to AI‑driven agents.
Martin Fowler argues that software engineering has moved from a deterministic world to a nondeterministic one driven by LLMs, outlining how this paradigm shift reshapes development practices, introduces new risks, and demands a harness‑based engineering approach to manage uncertainty.
This article walks through a complete Agentic Engineering workflow for a backend Go service, showing how AI‑driven Skills and Commands automate requirement capture, clarification, planning, parallel development, code review, deployment, log analysis, MR creation, AI‑assisted review, automated fixes, and final merge, while highlighting token consumption and practical lessons.
This guide distills the most impactful 12 tips from the Claude Code best‑practice repository, covering file length limits, permission‑restricted agents, context isolation, hooks, sandboxing, parallel instances, extended reasoning, scheduled loops, side‑question handling, Gotchas documentation, completion verification, and the --bare SDK flag to help developers fully leverage Claude's AI coding capabilities.
A year after Andrej Karpathy coined “vibe coding,” the AI‑driven programming boom has triggered a wave of low‑quality contributions, security regressions, and open‑source maintainer backlash, prompting a data‑backed shift toward disciplined “agentic engineering” practices.
The guide reveals that while AI agents have reduced code generation costs to near zero, the true expense lies in ensuring quality, requiring engineers to shift from writing code to defining problems, designing agentic systems, and applying rigorous testing patterns such as red‑green TDD, context‑managed sub‑agents, and advanced Git workflows.
The article examines the shift from Vibe Coding to Agentic Engineering, showing that overloading AI with too many tools confuses it, and argues that developers must evolve from code writers to AI orchestrators who guide and supervise intelligent agents rather than merely issuing commands.
Z.ai released the open‑source GLM‑5 model with 744 billion parameters, 28.5 T tokens of training data, and new Sparse Attention and Slime RL infrastructure, achieving top open‑source rankings and near‑Claude Opus 4.5 performance on Vending Bench 2 and CC‑Bench‑V2 while adding multi‑scenario agent capabilities.
This article explains the Agentic Engineering paradigm, outlining its three pillars, team‑level strategy, a detailed five‑step AI‑powered TDD workflow, role‑specific action items, and the broader shift from coding to commanding software development.
A year after introducing Vibe Coding, Andrej Karpathy reflects on the shift from casual AI‑assisted snippets to professional Agentic Engineering, highlighting how large language model agents now orchestrate code while demanding oversight to maintain software quality.
In this interview, veteran iOS developer Peter Steinberger explains how his open‑source project Clawdbot (now Moltbot) evolved from a personal need for an autonomous assistant, detailing its rapid GitHub growth, WhatsApp integration, CLI‑first philosophy, security considerations, and his vision for a future where personal AI agents replace traditional apps.
An engineer recounts the hard‑earned lessons from moving beyond RAG to enterprise‑level AI agents, exposing three critical challenges—scheduling, memory management, and tool integration—and proposes architectural patterns that turn fragile prototypes into robust, observable, and secure AI systems.
