After a year of ad-hoc RAG solutions, agent memory is becoming a proper engineering discipline. Four independent research efforts outline budget tiers, shared memory banks, empirical grounding, and temporal awareness — the building blocks of a real memory architecture.
The recent introduction of “reasoning tokens” in frontier language models represents a subtle but significant shift in how these systems approach complex problems. For years, we have interacted with AI that provides direct answers. We ask a question, and it generates a response. But what if the most important work
The conversation around AI is shifting from passive chatbots to active, autonomous agents. We have explored how new architectural patterns like reasoning tokens and external memory are giving these agents the ability to “think” and “remember.” But how do you go from understanding these concepts to building a functional agent
We have all experienced it. You are in the middle of a promising conversation with an AI assistant, meticulously explaining the nuances of a project. You close the tab, and when you return, the AI greets you with a blank stare. It has forgotten everything. This is the “goldfish brain”
