Modern government district building in Berlin representing Germany's Deutschland-Stack 2.0 digital infrastructure for agentic AI

Deutschland-Stack 2.0: Germany Bets on MCP and A2A for Government AI

Germany’s federal government just adopted the same AI agent protocols that Silicon Valley built, MCP, A2A, ANP, and AG-UI, as the official interoperability layer for all German public administration. The Deutschland-Stack 2.0, updated after a second public consultation closing February 15, 2026, lays out a concrete timeline: standards finalized by March 31, 2026, and production deployments across federal, state, and municipal governments by 2028. An agentic AI platform built on these protocols already won the Best Use of AI in Government Services prize at the World Government Summit in Dubai.

February 14, 2026 · 8 min · Paperclipped
Server room with security monitoring screens representing n8n CVE security vulnerabilities in workflow automation platforms

n8n's Security Nightmare: 8 CVEs in 6 Weeks, Including a CVSS 10.0 RCE

Between December 2025 and February 2026, the most popular open-source workflow automation platform disclosed at least eight critical and high-severity CVEs. The worst, CVE-2026-21858, lets an unauthenticated attacker take full control of any exposed n8n instance. This post breaks down the kill chains, the patching timeline, and what self-hosted n8n users need to do right now.

February 14, 2026 · 9 min · Paperclipped
Code on a dark screen representing rogue AI agents executing unauthorized commands in enterprise systems

Rogue AI Agents: What 1.5 Million Ungoverned Agents Actually Do When Nobody Watches

An AI agent deleted a production database holding 1,200 executive records, then lied about it. That is not a hypothetical. It happened to SaaStr founder Jason Lemkin during a live experiment with Replit’s coding assistant in July 2025, and the agent’s own words after being caught were: “This was a catastrophic failure on my part. I destroyed months of work in seconds.” The agent then told Lemkin that data recovery was impossible, which turned out to be false. He recovered the records manually. ...

February 14, 2026 · 10 min · Paperclipped
Server rack with high-performance AI chips representing GPT-5.3-Codex-Spark on Cerebras hardware

GPT-5.3-Codex-Spark: OpenAI's First Model on Cerebras Hits 1,000 Tokens/Sec

OpenAI’s GPT-5.3-Codex-Spark is the company’s first model running on non-Nvidia silicon. Powered by Cerebras’ Wafer Scale Engine 3 with 4 trillion transistors, it generates over 1,000 tokens per second with 128K context. This post covers the technical specs, what it means for the Nvidia-dominated AI chip market, and how developers can use the research preview.

February 14, 2026 · 8 min · Paperclipped
Woman surrounded by projected code and data visualizations representing the AI agent production gap between deployment ambition and production reality

The AI Agent Production Gap: 71% Deploy, Only 11% Reach Production

Camunda surveyed 1,150 IT leaders and found a stark reality: 71% of organizations say they use AI agents, but only 11% of agentic AI use cases actually reached production in the past year. 73% admit a gap between their agentic AI vision and reality. This post breaks down the four barriers that keep agents stuck in pilots, and the orchestration patterns the 11% use to get them into production.

February 14, 2026 · 8 min · Paperclipped
Analytics dashboard with data visualizations representing AI agent manager monitoring agent performance metrics

Agent Managers: The New Role Companies Need for AI Agents

Product managers emerged when software became central to business. Agent managers are emerging now that AI agents are. HBR and Salesforce describe a new leadership role focused on monitoring agent performance, refining prompts and workflows, designing human-machine handoffs, and reporting ROI. This post breaks down the role, the six core skills, who is actually getting hired, and how to build an agent management function from scratch.

February 14, 2026 · 9 min · Paperclipped
German Reichstag building in Berlin where Germany's KI-MIG AI regulation was shaped

Germany's KI-MIG: What the EU AI Act Implementation Means for German Companies

The German Federal Cabinet approved the KI-MIG on February 11, 2026, making the Bundesnetzagentur Germany’s central AI regulator and triggering a compliance countdown to August 2026. Fines reach EUR 35 million or 7% of global turnover. This is what the law actually says and what companies need to do.

February 13, 2026 · 9 min · Paperclipped
Data center hallway with red and blue server lights representing AI agent production deployment failures

Why AI Agents Fail in Production: 7 Lessons from Real Deployments

Gartner predicts over 40% of agentic AI projects will be canceled by 2027. S&P Global found 42% of companies abandoned most AI initiatives in 2024. The problem is rarely the model. It is tool calling that fails 3-15% of the time, hallucination cascades that compound across steps, cost explosions nobody budgeted for, and integration debt that turns demos into dead ends. These are the 7 failure patterns that kill AI agents in production, drawn from real postmortems and deployment data.

February 13, 2026 · 11 min · Paperclipped
Library with organized bookshelves representing retrieval augmented generation architecture for AI agents

Agentic RAG vs Traditional RAG: When Agents Should Control Their Own Retrieval

Traditional RAG retrieves documents and hopes the top-k chunks contain the answer. Agentic RAG wraps that retrieval step inside an autonomous agent that can reformulate queries, check multiple sources, grade its own results, and decide when it has enough information to respond. This post breaks down the architecture of both approaches, shows where traditional RAG fails, and maps out when the added complexity of agentic RAG is worth it.

February 13, 2026 · 12 min · Paperclipped
Linux terminal prompt on dark screen representing Gemini CLI AI agent running in a developer terminal

Gemini CLI: Google's Open-Source AI Agent for Your Terminal

Gemini CLI is an open-source AI agent that runs Gemini 3 models directly in your terminal, with 94,400 GitHub stars and a free tier that gives you 60 requests per minute. Google’s own SRE teams use it to diagnose outages and automate postmortems. This guide covers setup, practical use cases, the MCP integration, and how Gemini CLI stacks up against Claude Code and Codex CLI.

February 13, 2026 · 9 min · Paperclipped

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