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The expensive part of an AI agent usually isn't the final answer: it's the string of small decisions behind it. Every tool pick and "are we done yet" check is its own LLM reasoning call, and agent loops make dozens per task. Jev is a typed decision layer built to replace exactly those calls. Here's what that looks like inside a personal finance agent answering a spending question, and what it's worth evaluating for your own MCP stack. Key Takeaways Jev never executes anything: it only routes, labels, or checks; your existing LLM and code still do the real work. In a finance agent, it turns a spending question into a routed, labeled, fact-checked answer without extra reasoning calls. Adoption is shadow-mode first: test it against your current routing before it touches production traffic What Is Jev, and Why Are AI Teams Adopting It Now? Jev is a fast, typed decision model: send it a state and bounded questions, and it answers each in one of three fixed shapes: ...

  Every AI agent that touches the outside world does it one of two ways: it runs a shell command, or it calls a tool through the Model Context Protocol. Both get the job done, but they don't cost the same or fail the same way, which is why "MCP vs CLI" keeps resurfacing without resolving. I want to answer it for the cases that matter most: plain developer tooling, data-heavy systems like banking, forecasting pipelines, and transaction automation like billing or booking. Neither wins outright. Where you draw the line is the actual skill. Key Takeaways CLI is the default for anything the model already knows cold, git, file ops, text processing, since there's no schema tax and commands chain through pipes. MCP earns its cost when a system needs per-user auth, audit trails, or access to sources the model can't otherwise reach, exactly what regulated systems like banking require. Transaction automation works best split: deterministic code drives the repeatable steps, a...

At the AI Engineer World's Fair on July 1, 2026, Philipp Schmid, a Staff Engineer on the Gemini API and agents team at Google DeepMind, asked a room full of engineers a simple question: who uses skills with their coding agents? Every hand went up. Who has evals for those skills? Almost none. That gap is the entire talk, which Schmid also wrote up on his own blog, philschmid.de/testing-skills . His team indexed the skills ecosystem through SkillsBench and found the same pattern everywhere: people ship a SKILL.md file after two manual test runs and move on. It looks fine in a demo. It quietly corrupts outputs in production, because bad skills don't crash. They just make the agent confidently wrong. If you've already read our breakdown of Matt Pocock's Claude Code skills library , think of this as the other half of that story: what happens once you've installed a skill and it's actually running against real prompts. Key Takeaways SkillsBench indexed 47,000+ un...

Everyone is building agents right now. Real estate firms. Independent insurance brokers. Fortune 500 companies with budgets big enough to hire an army of consultants. Ask around and you'll hear the same story everywhere: "we're building our own agent." And yet almost nobody is asking the obvious question: why does the default approach keep failing? One large, general-purpose agent gets wired up to every tool the business owns. It impresses in the demo. Then it quietly stalls before production. There's a gap between what businesses want and what they're actually getting. They want AI woven into their data, their workflows, their day-to-day operations. What they get instead is one oversized agent trying to be a sales rep, a compliance officer, and a customer support line, all at once. That gap is an architecture problem, not a model problem. Key Takeaways The default "one big agent" pattern breaks down on context bloat, cost, fragility, and portability...

You sit down with Claude Code. You've got a plan. Three hours in, the context window is full, the conversation is tangled, and you've built half of the wrong thing. That's not a Claude problem. It's a workflow problem — and it's exactly what Matt Pocock designed his open-source skills library to fix. Pocock, best known as the creator of Total TypeScript, published these skills straight from his own .claude directory with a clear pitch: "Skills for Real Engineers." No bloated process frameworks. No opinionated orchestration. Just small, composable slash commands you can hack and extend. In June 2026, the mattpocock/skills repo has 150,773 GitHub stars and 13,032 forks — accumulated in under 5 months since its February 2026 release. That's the kind of adoption that tells you developers are hitting the same walls and finding the same fixes. This post covers the three productivity skills: grill-me , handoff , and teach . Key Takeaways As of June 2026, th...