Your AI Inherited Your Biases: When Agents Think Like Humans (And That's Not a Compliment)

New research shows AI agents don't just learn human capabilities — they systematically inherit human cognitive biases. The implications for deploying agents as objective decision-makers are uncomfortable.

Agents That Rewrite Themselves: Evolution Meets Artificial Intelligence

Three independent papers demonstrate agents rewriting their own training code, generating their own knowledge structures, and refining their reasoning at test time. Self-improvement has moved from theory to working engineering.

The Red Team That Never Sleeps: When Small Models Attack Large Ones

Automated adversarial tools are emerging where small, cheap models systematically find vulnerabilities in frontier models. The safety landscape is shifting from pre-deployment testing to continuous monitoring.

From Goldfish to Elephant: How Agent Memory Finally Got an Architecture

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.

From Answer to Insight: Why Reasoning Tokens Are a Quiet Revolution in AI

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

From Prompt to Partner: A Practical Guide to Building Your First AI Agent

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