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Im Building AI Agents: My Journey Beyond Prompt Engineering

Hey everyone, Alex here from agntai.net. It’s March 25th, 2026, and I’ve been wrestling with something pretty fundamental lately: how we actually *build* these AI agents. Not just the shiny LLM bits, but the whole messy structure that lets them do anything useful in the real world. We’ve moved past the “prompt engineering is all

AI/ML

Agent Architecture: What You Keep Getting Wrong

I’ve Committed Agent Architecture Crimes, Too

Look, I’ll admit it. I’ve been guilty of some heinous crimes against agent architecture. And you know what? I see the same mistakes all the time when folks talk about building these systems. Remember the time I sent out an agent to handle a simple automated process, and it snowballed

Applications

Im Tackling My Messy AI Agent Deployments Now

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

Performance

How To Stop Misjudging Agents: Evaluation Secrets

The Agony of Evaluating Agents Wrongly

You know that gut-wrenching feeling when you deploy a seemingly perfect agent system, only for it to crash and burn in a live scenario? I’ve been there too many times. It’s like investing in a hamster to defend your fortress. Useless. I remember back in October 2022, I deployed

Machine Learning

Haystack Pricing in 2026: The Costs Nobody Mentions

After 4 months wrestling with Haystack in a medium-scale search project: the headline is, “Haystack pricing looks cheap, but hidden costs will empty your pockets faster than you think.”

Let me cut to the chase before you dream up architectures: Haystack’s pricing model is messier than a spaghetti junction. The open-source deepset-ai/haystack framework itself is

Machine Learning

FastAPI vs Express vs Hono: Backend Showdown

FastAPI vs Express vs Hono: Backend Showdown
FastAPI currently holds a remarkable 96,522 GitHub stars. Express, a long-standing favorite, has a substantial following but lags behind FastAPI in this regard. Hono, the new kid on the block, is just starting to gather steam. But let’s not kid ourselves: stars don’t ship features, and when it

Applications

My AI Agents Memory: Solving Bloat & Slowness

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

Machine Learning

Activepieces vs Windmill: Which One for Side Projects

Activepieces vs Windmill: Which One for Side Projects

Activepieces is gaining traction with around 8,400 users across its platforms, while Windmill has amassed a user base of about 10,000. Numbers are interesting, but they only represent the tip of the iceberg when choosing tools for your side projects. I’ve used both and have my thoughts,

Machine Learning

How to Set Up Ci/Cd with Milvus (Step by Step)

How to Set Up CI/CD with Milvus: A Step-by-Step Guide

Setting up CI/CD with Milvus can seem daunting, but it doesn’t have to be. In this tutorial, we’re building a system that streamlines the deployment of Milvus, a vector database that currently boasts 43,455 stars, 3,912 forks, and has 1,085 open issues on GitHub, which

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