Insights on AI, Agents & Automation
Practical thinking on AI engineering, automation strategy, and what it actually takes to ship AI systems that work.
How To Use AI For Data Integration: A Practical Guide For 2026
If you’ve watched an AI agent confidently write a SQL query that joins the wrong tables and inflates revenue by 3x, you already know the core problem with AI data integration. The models don’t know they’re wrong. According to SWIRL AI’s 2025 analysis, close to 40% of text-to-SQL queries produced by LLMs contain errors, and
What Is MLOps? A Working Definition for People Who Have to Ship Models
Roughly 88% of corporate machine learning projects never reach production. That number, cited by Databricks, is the whole reason MLOps exists as a discipline. Models behave differently from software. They rot. The data underneath them shifts, the world moves on, and the shiny notebook that scored 94% accuracy in April is quietly making bad calls
8 Best AI Note Takers for Meetings, Sales, and Study (2026)
The AI note-taking category split apart in 2026. What used to be a race for transcription accuracy is now four separate races: sales teams want CRM automation, students want flashcards, privacy people want local processing, and anyone doing in-person meetings wants hardware that actually hears the room. The best AI note taker for you depends
7 DevOps Best Practices That Actually Matter in 2026
Most DevOps advice reads like it was written in 2018. Pipelines, containers, CI/CD, done. That framing is now badly out of date. As of July 2026, the practices separating teams that ship reliably from teams drowning in incidents are shift-left disciplines (testing, security, and cost), event-driven drift control, platform engineering, and the early moves toward
8 Best AI Customer Support Agents in 2026
If you’re shopping for an AI customer support agent right now, start with Intercom Fin. It leads independent benchmarks at a 76% average resolution rate, prices at $0.99 per outcome, and ships with its own helpdesk so you’re not paying two vendors to handle the same ticket, plus that last part matters more than most
What Are Agentic Workflows , and Why They Break Traditional Automation
An agentic workflow is a goal-oriented system where AI agents decide at runtime how to accomplish a task, rather than following a fixed script. That single shift, from “run these steps” to “hit this outcome within these limits,” is why 78% of enterprises now have pilots but only 14% have hit production scale. This piece
Best LLM for Coding 2026: Which Model Actually Wins on Your Codebase
Picking the best LLM for coding in 2026 isn’t a single-model question anymore. It’s a routing question. Right now, Claude Opus 4.8 leads on complex repository work, GPT-5.5 owns terminal and greenfield tasks, and DeepSeek V4 Pro has quietly closed the gap for teams that need to self-host. If you only remember one thing from
Generative AI for Supply Chain: What Actually Works in 2026 (and What’s Failing at Scale)
Here’s the number that should stop any supply chain leader mid-sentence: Gartner expects more than 40% of agentic AI projects to be cancelled by 2027, and roughly 88% of AI proofs-of-concept never reach production at all. That’s not a hype-cycle wobble. That’s a message about where generative AI for supply chain actually breaks. This article
9 AI Agent Use Cases That Actually Work in Production (2026)
Most AI agent lists read like vendor brochures. This one doesn’t. As of 2026, only 14% of enterprises with active agent pilots have reached production scale, so the useful question isn’t “what could agents do?” but “what are they actually doing, at what ROI, and where do they break?” Below are nine ai agent use
What Is Context Engineering? The Discipline That Replaced Prompt Engineering
Model correctness starts falling apart around 32,000 tokens, even on models advertising context windows of 1 to 2 million. That single fact explains why the phrase “just paste the whole codebase in” quietly stopped working sometime in 2025, and why context engineering, not prompt engineering, is now the deciding factor for whether an AI agent
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- 01One agent build, taken apart step by step
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Written by Ignas Vaitukaitis, founder of AlphaCorp AI.