Tag

AI Engineering

AI Automation

Nine Production Automations Have 31 Endings. Fewer Than Half Are the Happy Path

Almost nobody publishes the architecture of the automations they deliver, so almost nobody can say what one actually looks like. We publish nine, which means we can count them. Across 139 nodes and 148 edges in nine sectors, the systems fan out rather than funnel: 15 ways in against 31 ways out. And of those 31 endings, only 15 are the thing the system was built to do. The rest are refusals, holds, escalations and self-maintenance.

IrenicTech · Sep 23, 2026 · 7 min read

Agentic AI

AI Agent Development in 2026: From First Prototype to Production

Most writing about AI agents is either a demo or a warning. This is the working guide: what an agent is made of, how it reaches your systems through MCP, when one agent should become several, how much the model choice actually matters, why the majority never reach production, and what governance has to be in place before any of it is load-bearing. Every section links to the deeper piece on that question.

IrenicTech · Sep 21, 2026 · 16 min read

AI Automation

We Have 21 Decision Points in Production. Jev Would Change Six of Them

TypeSafe AI released a model that cannot hold a conversation, which is the point of it. Jev takes unstructured state in and returns a typed decision with a calibrated probability, in 70ms to 500ms. We went through the 21 decision nodes in our published automation teardowns to see where a model like this actually belongs. The answer is six of them, and the reasoning matters more than the number.

IrenicTech · Sep 21, 2026 · 7 min read

Agentic AI

What Is Agentic AI Development in 2026? Orchestration, MCP, and Multi-Agent Teams Explained

Agentic AI development moved from autocomplete to orchestration in 2026. Here's what an AI agent actually is, where MCP and multi-agent teams fit, and what it means for building real software, without the hype.

IrenicTech · Jun 28, 2026 · 6 min read

Austin

Why Austin's Record Startup Boom Is an AI Software Opportunity

Austin startups pulled in a record amount of venture funding in 2025, and most of it followed AI. Here's what that wave actually means for the companies riding it, and why funded ambition runs straight into a software-building problem.

IrenicTech · Jun 28, 2026 · 4 min read

AI Coding Agents

AI Coding Agents Compared in 2026: Claude Code, Cursor, Codex, Gemini CLI, and OpenCode

Claude Code, Cursor, Codex CLI, Gemini CLI, and OpenCode are the agents developers actually reach for in 2026. Here's an honest comparison of where each fits, plus how to read the benchmark and popularity numbers without being misled.

IrenicTech · Jun 28, 2026 · 6 min read

Open Source LLMs

The Best Open-Source LLMs in 2026: GLM, DeepSeek, Qwen, Kimi, and MiniMax Compared

The open-weight LLM field flipped to Chinese labs in 2026, and the leaderboards are a mess of vendor-claimed numbers. This is an honest, use-case-first comparison of GLM, DeepSeek, Qwen, Kimi, and MiniMax, with the caveats that actually matter before you build on one.

IrenicTech · Jun 28, 2026 · 6 min read

Multi-Agent Systems

Multi-Agent Systems: How Teams of AI Agents Are Starting to Run Software Work in 2026

One AI agent is useful. A coordinated team of them is a different kind of power, and a different kind of risk. Here's what multi-agent systems actually are, where they earn their keep, and what it takes to run them without creating chaos.

IrenicTech · Jun 28, 2026 · 4 min read

Open Source LLMs

Should You Use Open-Source LLMs for Your Product? Cost, Control, and the Chinese-Model Question

Open-weight models got genuinely good in 2026, and a lot of the best are now Chinese. Here's a practical way to decide whether open or closed models fit your product, weighing cost, control, data residency, and the things people get wrong about both.

IrenicTech · Jun 28, 2026 · 4 min read

MCP

MCP Explained: The Protocol That Makes AI Agents Useful, and How to Govern It

MCP is the standard that lets AI agents actually use your tools and data instead of just talking about them. Here's what it is, why it took over in 2026, and why connecting agents to your systems is now a governance problem too.

IrenicTech · Jun 28, 2026 · 4 min read

AI-Native SaaS

AI-Native Vertical SaaS: Building One That Survives Its First Enterprise Deal

AI-native vertical SaaS startups are reaching $100M ARR faster than any prior generation. Here's what separates a real AI-native product from AI bolted onto a CRUD app, and how to build one that survives its first enterprise deal.

IrenicTech · Jun 16, 2026 · 3 min read

Agentic AI

Most AI Agents Never Reach Production. Here's What Separates the Ones That Do

Pilots are easy; production is where AI agents go to die. Here's why most never make it, and the unglamorous engineering, evals, guardrails, feedback loops, that separates the agents that ship from the ones that stall.

IrenicTech · Jun 13, 2026 · 3 min read

Claude Fable 5

Claude Fable 5: Anthropic's Most Capable Public Model Is Here

Anthropic's Claude Fable 5 is its most capable public model yet. Here's what shipped, the new fallback safeguards, pricing, and what it means if you're building AI products.

IrenicTech · Jun 11, 2026 · 4 min read

AI Engineering

How We Ship AI-Native Products in Weeks, Not Quarters

Why we scope AI like a product, build the feedback loop first, and ship secure AI in weeks instead of quarters.

IrenicTech · Jun 5, 2026 · 2 min read