Production ML: The Good, The Bad, and The Utterly Annoying
Ever spent six months chiseling away at a masterpiece of machine learning, only to see it crumble when it’s time to go live? Welcome to the club. It’s like pulling off the glittery ribbon, but the present explodes. My first stab at getting a chatbot
Alright, folks, Alex Petrov here, fresh from wrestling with a particularly stubborn LLM-as-a-brain for a new agent project. And that, my friends, brings us to today’s topic. We’re not just talking about agents; we’re diving deep into something I’ve seen trip up even experienced teams: the art and science of state management in complex AI
Conversation Management Checklist: 7 Things Before Going to Production
I’ve seen 3 production agent deployments fail this month. All 3 made the same 5 mistakes. This isn’t just bad luck or the universe playing tricks; it’s a problem of poor conversation management. Before you ship your next product or system into the wild, having a
Agent Debugging: A Developer’s Honest Guide
I’ve seen 3 production agent deployments fail this month. All 3 made the same 5 mistakes. If you’re working with AI agents, the debugging process can feel like navigating a minefield while blindfolded. Yet, it doesn’t have to be that way. This agent debugging guide is aimed to help
Why Are We Still Doing This?
Let me tell you about the time I almost smashed my laptop against the wall. Picture this: It’s a Friday night in 2025, and I’m debugging an agent system that was built by someone who, clearly, loved spaghetti more than clean architecture. Every single function was like an octopus—tentacles
Hey everyone, Alex here from agntai.net. It’s Friday, March 21st, 2026, and I’ve been wrestling with a particular problem in AI agent development lately that I think many of you might be encountering too. We’ve all seen the incredible demos of agents that can browse the web, write code, and even manage complex projects. But
After testing Weaviate for 14 months at enterprise scale: the advertised prices are just the starting point of your bill, buckle up.
I’ve been running Weaviate as part of a production vector search infrastructure since early 2025, dealing with millions of entries and complex query demands. During that time, I’ve seen firsthand where the advertised
Weaviate Add Memory to Your Agent: A 2500-Word No-Nonsense Tutorial
If you want your intelligent agent to actually remember the context between conversations, you need to weaviate add memory to your agent the right way, using vector search to store and recall previous interactions. We’re not just tossing snippets into some database; we’re building an
A Rant on Deployment Nightmares
Alright, let’s cut to the chase. You know what really grinds my gears when it comes to machine learning? People think deploying a model is just like clicking “Start” and poof, magic happens. Spoiler alert: it doesn’t. I’ve lost count of the times when a model, which performed impeccably well
Hey there, AgntAI.net readers! Alex Petrov here, and today I want to talk about something that’s been rattling around my brain for a while now: the surprisingly subtle but critical shift in how we think about agent memory. Forget your fancy new model architectures for a minute; I’m talking about the mundane, often overlooked details