While a primary Large Language Model (LLM) agent—one which repeatedly calls exterior instruments—is simple to create, these brokers usually battle with lengthy and complicated duties as a result of they lack the flexibility to plan forward and handle their work over time. They will be thought-about “shallow” in their execution. The deepagents library is designed…
Anthropic just lately launched a guide on effective Context Engineering for AI Agents — a reminder that context is a important but restricted useful resource. The high quality of an agent typically relies upon much less on the mannequin itself and extra on how its context is structured and managed. Even a weaker LLM can…
Kong has open-sourced Volcano, a TypeScript SDK that composes multi-step agent workflows throughout a number of LLM suppliers with native Model Context Protocol (MCP) software use. The launch coincides with broader MCP capabilities in Kong AI Gateway and Konnect, positioning Volcano because the developer SDK in an MCP-governed management aircraft. Why Volcano SDK? as a result…
Agentic methods are stochastic, context-dependent, and policy-bounded. Conventional QA—unit exams, static prompts, or scalar “LLM-as-a-judge” scores—fails to show multi-turn vulnerabilities and gives 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…
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…
In this tutorial, we discover the best way to construct a Context-Folding LLM Agent that effectively solves lengthy, complicated duties by intelligently managing restricted context. We design the agent to interrupt down a massive job into smaller subtasks, carry out reasoning or calculations when wanted, and then fold every accomplished sub-trajectory into concise summaries. By…
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/…
In this tutorial, we discover safe AI brokers in sensible, hands-on methods utilizing Python. We deal with constructing an clever but accountable agent that adheres to security guidelines when interacting with knowledge and instruments. We implement a number of layers of safety, akin to enter sanitization, prompt-injection detection, PII redaction, URL allowlisting, and charge limiting,…