2026
Tahir defines an agent as the combination of a model and a harness, the program that maps model tokens to actions.
“And the the outcome that comes out of combining the harness and the model is the agent, right?”
Topic
Hamza Tahir
7 facts from 1 episode, in date order. These are what speakers said on the shows, each linked to the moment it was said. They aren't independently verified.
2026
Tahir says moving agents from laptops to cloud infrastructure is underway in 2026, with adoption described as slow and then sudden.
“it's like this sort of slow adoption, and then suddenly boom, there's like a crazy increase.”
Undated
Hamza Tahir says he co-founded ZenML about five years before this episode, when MLOps interest was at a peak.
“So I co founded ZenML about five years ago, so this was really, almost, um, at a point where ML Ops was really reaching fever pitch”
Undated
Hamza Tahir says he spent four or five years deploying ML models at a previous company he co-founded before starting ZenML.
“And I had done four or five years of that in my previous job, where I was co founding um, another company, uh, trying to deploy ML models”
Undated
Tahir describes a large language model as a token generator that cannot take actions on its own.
“The LLM model is simply a token generator, so in itself it doesn't do anything, it doesn't do action.”
Undated
Tahir says action-taking by models began about three years earlier with structured outputs and tool calling.
“So the way we did action was we started three years ago with structured outputs and tool calling”
Undated
Tahir describes a standard architecture where an API server places events on a message broker and workers process them, so tasks survive worker failures.
“So a very normal one o one architecture is just putting a message queue, a message broker in the middle of that.”
Episodes