Articles

Most engineers think a harness manages tool calls. But it also decides what the model sees, what stays outside its working memory, how work is delegated, and what must be verified before action. This article turns early RLM research into a practical way to redesign long-running coding agents.

Your AI agent needs to read emails, webpages, GitHub issues, and documents to get useful work done. The problem begins when material it was supposed to examine starts influencing what it does next. This article shows where that risk appears in everyday agent workflows and how to keep permissions outside the model.

An AI art company, Midjorney, just walked into medical imaging. The real lesson isn't about the scanner — it's about where disruption now comes from, and whether the incumbents are ready for it.

AI can speed up coding without improving delivery. Learn how teams can find where work waits, measure real gains, and fix the next bottleneck. Here is a practical guide for engineering leaders, Scrum Masters, project managers, and delivery teams separating faster coding from real productivity gains.

Most first agent demos do not fail because the model forgot something; they fail because the system cannot tell current workflow progress from reusable memory. The bug appears when a user corrects the agent, a tool fails, or an old preference conflicts with the current source of truth. This article gives developers a first-principles boundary for building agents that can recover from corrections without corrupting future runs.

Karpathy’s Anthropic move is a career lesson hiding in plain sight: the badge is visible, but the work compounds. This article explores why title inflation, dry promotions, and status-heavy designations can mislead professionals — and how to judge roles by autonomy, scope, authority, and leverage instead. The punchline: don’t chase the loudest title; chase the biggest room to grow.

Android teams are using LLMs to do codebase health checks — reviewing dependency hygiene, catching security misconfigurations, flagging deprecated APIs, checking Play policy compliance. The results are disappointing. The findings feel shallow, vary run to run, and miss issues a senior engineer would catch in minutes. The model is not the problem. The project is not LLM-friendly — and making it LLM-friendly is the new engineering discipline that most teams have not yet tackled.

Most teams reach for multi-agent architecture before a single agent has hit a real limit. A 2025 Google DeepMind study found that unstructured multi-agent networks produce up to 17.2× error amplification — more agents, without structure, compounds errors rather than reducing them. This article gives you a practical decision tree for knowing when one agent is genuinely enough, and what specifically justifies climbing to the next level.

AI agents that browse, click, and act inside your internal tools are no longer a research demo. They are in production — and most enterprise teams built guardrails for API calls, not autonomous cursors. Here is what needs to change before your next deployment.

Every team evaluating AI coding agents reaches for the same benchmark table. The scores look rigorous. The methodology footnotes look serious. But benchmarks measure generation — your team pays for everything that happens after. This article is about building the measurement that benchmark tables forgot: accepted, reviewed, tested work per dollar and per engineer-hour.

A technical post-mortem on what went wrong, why guardrails failed, and how engineering teams can safely use AI coding agents without handing them the keys to production.

Google Cloud Next 2026 was not just about launching more AI features. Read as a whole, the announcements point to a broader shift: enterprise AI is becoming a full-stack inference-delivery problem spanning the front door, agent control plane, model layer, data context layer, execution runtime, infrastructure, and governance.