Si alguna vez te has sumergido en evaluaciones de agentes, dándote golpes contra ellas, sabes que la lucha es real. He estado allí, gritando a mi portátil, tratando de averiguar si mi agente es realmente inteligente o solo otro aspirante a HAL 9000. Elegir las evaluaciones adecuadas puede ser la diferencia entre pensar que has creado algo
¿Alguna vez te has encontrado atrapado en un agujero de conejo depurando sistemas de agentes, solo para darte cuenta de que es un error tipográfico en tu código de manejo de errores? Oh hombre, bienvenido al club. Construir tuberías de agentes que no implosionen no es un paseo por el campo. Confía en mí en esto; he pasado fines de semana enteros golpeando mi cabeza contra el teclado,
El mes pasado, estaba a punto de rendirme con respecto a hacer que los transformers funcionaran bien con mi sistema de agentes. ¿Te ha pasado? Esos maratones interminables de depuración realmente pueden agotar tu voluntad de vivir. Pero luego, me topé con este truco increíble de Hugging Face Transformers. Honestamente, ese momento de “¡Eureka!” es
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
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
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
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
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
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
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