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,
Hey there, AgntAI.net readers! Alex Petrov here, fresh off a particularly gnarly debugging session that got me thinking. We talk a lot about the grand vision of AI agents – the autonomous systems that can plan, execute, and adapt. But what about the messy reality of building them? Specifically, the part where they need to
I remember the first time I tried to build an agent system. I thought I was a genius, piecing together a bunch of pre-made solutions and half-baked code snippets. But, guess what? My “masterpiece” was a sluggish, inefficient mess. Zero efficiency. Negative engagement. If you’ve ever tried constructing an
Hey everyone, Alex here from agntai.net. It’s March 17th, 2026, and I’ve been wrestling with a particular problem in agent design that I think many of you might be encountering as well. We’re all trying to build smarter, more autonomous AI agents, right? But the moment you start pushing for real-world complexity, the neatly compartmentalized
Agent Evaluation: Cutting Through the Noise
Just the other day, I was knee-deep in debugging yet another agent system when I realized how often we all skip proper evaluation. It’s like people are actively allergic to real feedback loops and thorough assessments! I’m sick of seeing releases where the agent is barely more intelligent than
Hey everyone, Alex here from agntai.net! It’s March 2026, and I’ve been spending way too much time lately thinking about how we build AI agents. Specifically, I’ve been wrestling with the “glue code” – the stuff that connects all the fancy LLM outputs, tool calls, and state management. We’ve all seen the impressive demos, right?
Dapo: An Open-Source LLM Reinforcement Learning System at Scale
As an ML engineer, I’ve seen firsthand the challenges of fine-tuning large language models (LLMs) for specific tasks. While supervised fine-tuning (SFT) is effective, it often falls short in aligning models with complex human preferences or nuanced real-world reward signals. This is where reinforcement learning from