Alright, folks, Alex Petrov here, back at agntai.net. It’s March 2026, and if you’re anything like me, your Slack channels and Twitter feeds are absolutely buzzing with discussions about AI agents. Not just the abstract “what ifs,” but the very real, very messy “how tos” of getting these things to actually do something useful without
Hey everyone, Alex here, back on agntai.net. It’s March 23rd, 2026, and I’ve been wrestling with a particular problem lately that I think many of you building AI agents are probably facing: how do you keep your agent’s long-term memory from becoming a bloated, slow, and ultimately useless mess?
We’ve all been there. You start
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
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
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
Alright, let me just get this off my chest first—RAG systems, or Reasoning and Generation systems, are not the golden goose everyone seems to think they are. Yeah, I’ve been tinkering with these for a while now, and to be honest, they’re more often a wild goose
Alright folks, Alex Petrov here, back at agntai.net. Today, I want to talk about something that’s been rattling around in my head for a while, especially after spending way too many late nights debugging an agent’s “understanding” of a simple task. We’re all building these AI agents, right? Autonomous systems, trying to get things done
A practical guide to moving ML models from notebooks to production, covering architecture choices, training pipelines, and deployment.
When a Cool Prototype Becomes a Total Disaster
So there I was, sipping my third coffee for the day, trying to untangle why our ML model was making the worst predictions possible. It’s a classic case: everything works great in the lab, then you throw it into production and BAM—chaos. If you’ve ever been here,