Agentic AI

In this tutorial, we construct an AI-powered cryptographic agent system that mixes the power of classical encryption with adaptive intelligence. We design brokers able to performing hybrid encryption with RSA and AES, producing digital signatures, detecting anomalies in message patterns, and intelligently recommending key rotations. As we progress, we witness these autonomous brokers securely set…

Agentic methods are stochastic, context-dependent, and policy-bounded. Conventional QA—unit assessments, static prompts, or scalar “LLM-as-a-judge” scores—fails to expose multi-turn vulnerabilities and offers weak audit trails. Developer groups want protocol-accurate conversations, express coverage checks, and machine-readable proof that may gate releases with confidence. Qualifire AI has open-sourced Rogue, a Python framework that evaluates AI brokers over…

How would your agent stack change if a coverage might practice purely from its personal outcome-grounded rollouts—no rewards, no demos—but beat imitation studying throughout eight benchmarks? Meta Superintelligence Labs suggest ‘Early Experience‘, a reward-free coaching method that improves coverage studying in language brokers without massive human demonstration units and without reinforcement studying (RL) in the…

Meta AI has launched Agents Research Environments (ARE), a modular simulation stack for creating and operating agent duties, and Gaia2, a follow-up benchmark to GAIA that evaluates brokers in dynamic, write-enabled settings. ARE offers abstractions for apps, environments, occasions, notifications, and eventualities; Gaia2 runs on high of ARE and focuses on capabilities past search-and-execute. https://ai.meta.com/analysis/publications/are-scaling-up-agent-environments-and-evaluations/…