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Notes from the team

Notes on building, evaluating and operating AI agents.

Guide

A version that gets worse doesn’t reach production

This is how Thaliq’s eval gate works: every change to an agent is measured before it is published. If the score doesn’t reach the threshold, the version already serving your customers stays where it is.

· 6 min read

Engineering

How your agent knows things it was never trained on

A model doesn’t know your return policy. Retrieval finds the right passages of your own content and hands them to the model with the question. Here is how Thaliq does it, step by step.

· 4 min read

Engineering

Anatomy of an agentic loop

An agent isn’t a prompt: it’s a loop that reasons, calls tools, observes and repeats until it can answer. Here is that loop, running.

· 4 min read

Product

Tokens at cost: what is in a Thaliq statement

Some platforms resell you the model with a margin on every token. A Thaliq paid plan is a monthly fee plus the tokens you use, at what they cost us, with no margin for now.

· 3 min read