Germany’s Fachkräftemangel costs the economy €49 billion per year and AI agents could absorb much of the administrative burden. But 94% of Mittelstand companies have no AI in production. The gap is organizational, regulatory, and cultural. This post breaks down the real barriers and what DACH companies are actually doing to close the gap.
Non-human identities outnumber human ones 144:1, and 47% of enterprise AI agents operate without governance. This post covers why traditional IAM fails for agents, what authorization bypass looks like in practice, and how to build an agent identity framework that actually works.
Traditional software tests check for exact outputs. AI agents produce different outputs every time. Anthropic recommends starting with 20-50 evals drawn from real failures. Sierra’s Tau-Bench shows even GPT-4o succeeds on fewer than 50% of real-world customer service tasks. Here is how to build a testing strategy that works.
Crypto.com CEO Kris Marszalek bought the most expensive domain name in history and launched a consumer AI agent platform during Super Bowl LX. The promises are big. The technical details are almost nonexistent. Here is what we know and what is missing.
GitHub Next coined Continuous AI: natural-language rules plus agentic reasoning, executed continuously inside your repository. With the gh aw CLI, Claude, Codex, and Copilot now run as first-class CI agents. One in seven GitHub PRs already involves an AI reviewer. Here is how the repository became the agent workspace.
On February 5, 2026, Gartner published its annual cybersecurity trends report and put agentic AI oversight at the top for the first time. With 40% of enterprise apps expected to embed task-specific agents by year-end (up from 5% in 2025), the security implications are massive. This post breaks down all six trends, the real incidents driving each one, and concrete steps security teams should take now.
LangChain’s State of Agent Engineering survey shows 57.3% of teams now run agents in production. But the gap between watching agents and testing them is wide: 89% have observability while only 52% evaluate. Coding agents dominate daily usage, quality remains the top barrier, and cost concerns are fading as latency takes their place.
The average enterprise runs 957 applications. Only 27% of them are connected to anything else. That single statistic, from the 2026 MuleSoft Connectivity Benchmark Report, explains why most AI agent projects stall. The bottleneck is not the model. It is not the prompt. It is decades of enterprise plumbing that was never designed for software that acts on its own. Organizations already operate an average of 12 AI agents, and 83% report that most teams have adopted them. But 50% of those agents run in isolated silos, disconnected from the systems they need to be useful. Gartner projects that over 40% of agentic AI projects will be canceled by end of 2027 due to escalating costs and unclear value. The infrastructure gap is the primary reason. ...
VS Code 1.109 ships parallel subagents, native Claude and Codex agent integration, agent orchestrations, terminal sandboxing, and Copilot Memory. Microsoft is positioning the editor not as a code tool with AI bolted on, but as a runtime for coordinating multiple AI agents across local, background, and cloud environments.
Gartner predicts 40% of agentic AI projects will be canceled by 2027. Klarna saved $39M, JPMorgan freed 360,000 legal hours. The difference between those outcomes is not the technology. It is how companies scope, fund, and measure their deployments.