This article proposes a 5-step framework, based on Conformal Prediction, to evaluate the trustworthiness of AI agents. It offers a mathematical guarantee for a provable reliability score, instead of relying on LLMs as judges.
The Hermes Agent guide explores advanced AI agent frameworks capable of planning, scheduling, and executing automated workflows. It highlights Nous Research’s Hermes Agent, offering a self-hosted runtime for building agents with state management and tool integration for real-world automation.
A founder of an AI answering service for trade contractors presents a framework for evaluating such services, acknowledging his bias. The article provides an in-depth operational guide on testing, instrumentation, negotiation, and common production issues, specifically for builders and operators.
This article introduces everything-claude-code, an open-source project designed as a comprehensive enhancement framework for Claude Code. Developed by AI engineer Affaan Mustafa, it systematically codifies advanced techniques through skills, sub-agents, and rules to optimize Claude's performance and capabilities.
This article introduces Pydantic AI, a new Python agent framework designed to address abstraction and control issues found in tools like LangChain and CrewAI. It highlights Pydantic AI's approach of defining agents as Python functions with typed inputs and outputs, aiming for structured outputs without the overhead of other frameworks.
Uma nova estrutura cognitiva está sendo introduzida para medir o progresso em direção à AGI. Para auxiliar no desenvolvimento das avaliações pertinentes, um hackathon no Kaggle será lançado.
LangGraph is a framework for building stateful and agentic LLM applications, particularly useful for workflows needing multiple steps, conditional routing, memory, and tool usage. It helps model an AI workflow as a graph where state tracks data and nodes represent actions.
LangChain.rb é a portabilidade da popular biblioteca LangChain para Ruby, simplificando o desenvolvimento de aplicações de IA ao oferecer abstrações pré-construídas. Ele fornece blocos de construção para padrões comuns de IA como clientes LLM, cadeias e agentes, eliminando a necessidade de conectar manualmente APIs e consultas.
Este conteúdo propõe um novo framework para a avaliação de agentes de voz, denominado EVA. O objetivo é estabelecer uma metodologia padronizada para medir a qualidade e o desempenho de sistemas de IA conversacional.
Este conteúdo anuncia a publicação de um artigo no The AI Blog intitulado 'Microsoft’s framework for building AI systems responsibly'. O artigo aborda a proposta da Microsoft para o desenvolvimento responsável de sistemas de inteligência artificial.