
A Inteligência Artificial Agêntica designa sistemas de IA que buscam objetivos de múltiplas etapas de forma autônoma, iterando por meio de ciclos de percepção, planejamento, uso de ferramentas e autocorreção, sem exigir intervenção humana em cada ponto de decisão — uma mudança estrutural em relação aos modelos de resposta rápida em direção a arquiteturas que decompõem objetivos complexos em subtarefas, executam ações em ambientes externos, avaliam os resultados e revisam os planos de acordo.
A arquitetura agentiva canônica acopla um núcleo de raciocínio baseado em um modelo de linguagem robusto com um registro de ferramentas — navegadores web, interpretadores de código, clientes de API, sistemas de arquivos, interfaces de banco de dados — e uma arquitetura de memória que abrange o contexto de trabalho de curto prazo, registros episódicos de ações passadas e conhecimento de longo prazo recuperado, permitindo que o agente mantenha a busca coerente por seus objetivos em horizontes de interação estendidos que ultrapassam qualquer janela de contexto individual. Configurações multiagentes ampliam ainda mais esse conceito, distribuindo subtarefas entre agentes especializados coordenados por um orquestrador, introduzindo a interação entre agentes. comunicação Protocolos, agregação de resultados e resolução de conflitos como preocupações arquitetônicas adicionais.
As publicações e patentes indexadas abaixo abordam algoritmos de planejamento, arquiteturas de uso de ferramentas, sistemas de memória, protocolos de coordenação multiagente, benchmarks de avaliação de agentes e metodologias de restrição de segurança.
Esta é a nossa mais recente seleção de publicações e patentes mundiais em inglês sobre IA Agética, provenientes de diversos periódicos científicos online, classificadas e focadas em IA Agética, agente de IA, agente de IA autônomo, sistema multiagente, orquestração de agentes de IA, ciclo de planejamento de agentes, agente ReAct, planejamento baseado em cadeia de pensamento, agente de IA que utiliza ferramentas, IA com chamada de funções, memória de agentes de IA, memória de longo prazo de agentes, memória episódica de agentes, memória de trabalho de agentes, decomposição de tarefas de agentes de IA, planejamento hierárquico de agentes, autocorreção de agentes de IA, reflexão de agentes de IA, avaliação de agentes de IA, modelagem de recompensas de agentes, sandboxing de agentes de IA, salvaguardas de segurança para agentes, coordenação multiagente, protocolo de comunicação de agentes, registro de ferramentas para agentes de IA, agente executor de código, agente de navegação na web, agente com recuperação aprimorada, avaliação de benchmarks de agentes e agente com interação humana.
Agentic ai system for dynamically determining optimal toolchain, and driving method therefor
Patent published on the 2026-05-28 in WO under Ref WO2026111487 by LG MANAGEMENT DEVELOPMENT INST CO LTD [KR] (Lee Hong Lak [kr], Sohn Sung Ryull [kr], Choi Ye Muk [kr])
Abstract: The present disclosure provides an agentic AI driving method for dynamically determining an optimal toolchain on the basis of an execution mode according to a user input and an expected latency of a toolchain DB. Accordingly, an external tool and an internal module are controlled to execute a main task and output a final response including the execution result.[...]
Our summary: The system dynamically determines the optimal toolchain based on user input and expected latency. An external tool and internal module are controlled to execute a main task. The final response includes the execution result.
Agentic AI, toolchain optimization, execution mode, latency management
Patent
Method and system for artificial intelligence (ai) agent training
Patent published on the 2026-05-28 in US under Ref US20260148085 by TATA CONSULTANCY SERVICES LTD [IN] (Sivakumar Narendran [gb], Ramasamy Venkada Subramanian [in], Viswanathan Sankaranarayanan [in], Kannan Radhika [in])
Abstract: [0000] AI systems, while have excelled in environments defined by clear rules and singular tasks, have been found to be struggling to handle Knowledge works which encompass tasks requiring judgment, interpretation, and creative problem-solving. Method and system disclosed herein provide an Artificial Intelligence (AI) agent training approach. In this approach, the system 100 achieves collaboration between different AI agents that are part of a network, for handling each task. During the training[...]
Our summary: This method enhances AI agent training by fostering collaboration among multiple agents. It addresses challenges in knowledge work that require judgment and creativity. The training utilizes dynamically decided actions based on activation levels of active agents.
AI training, collaborative agents, knowledge work, dynamic decision-making
Patent
Method for performing a task according to a flare model including a multi-modal planning module and an environment-adaptive replanning module and ai a
Patent published on the 2026-05-21 in US under Ref US20260141703 by UIF UNIV INDUSTRY FOUNDATION YONSEI UNIV [KR] (Kim Tae Woong [kr], Kim Byeonghwi [kr], Choi Jonghyun [kr])
Abstract: [0000] A method for performing a task according a FLARE model including a multi-modal planning module and an environment-adaptive replanning module is provided. The method of an AI agent includes steps of: (a) instructing the multi-modal planning module to calculate degrees of similarity between training data and a current pair comprised of natural language data and image data and acquire k natural language data by using the degrees of similarity; (b) instructing the multi-modal planning module [...]
Our summary: The method involves an AI agent utilizing a multi-modal planning module to analyze natural language and image data. It generates an initial action plan based on similarity degrees and adapts the plan using an environment-adaptive replanning module when necessary. The process ensures effective task performance even when target information is incomplete.
AI, multi-modal planning, environment-adaptive replanning, FLARE model
Patent
Computer-implemented method, computer system, data models, and computer program for simulating degradation, ageing, performance, and/or thermal behavi
Patent published on the 2026-05-20 in EP under Ref EP4745825 by BATTERY SPHERE GMBH [DE] (Lutz Lukas [de], Scherrer Luca [de], Alves Dalla Corte Daniel [de], Principe Victor [de])
Abstract: [0001] The invention relates to a computer-implemented method for simulating and predicting a degradation, ageing, performance, and/or thermal behavior of an electric battery, a method for generating corresponding training datasets, and a computer system for supporting users in different aspects of battery development, testing and validation. This battery domain specific artificial intelligence system (Battery AI System) applies raw data preparation steps like segmenting, timestamp data extracti[...]
Our summary: The invention describes a method and system for simulating battery degradation and performance. It utilizes transformer-based machine learning models for accurate predictions. A large language model facilitates user interaction with the Battery AI System.
simulation, battery, machine learning, degradation
Patent
Dynamic navigation generation using ai
Patent published on the 2026-05-07 in US under Ref US20260126295 by IBM [US] (Brew Kevin Wayne [us], Shah Priti Ashvin [us], Morillo Jaime D [us])
Abstract: [0000] A monitoring system includes a computer hardware system with a hardware processor configured to initiate the following executable operations. Communications from one or more communication devices associated with first responders are real-time monitored. Using an artificial intelligence (AI) agent analyzing the communications, an event is detected. Using the AI agent, an event location associated with the event is identified. Using the AI agent and the event location, an occlusion zone ass[...]
Our summary: The system monitors real-time communications from first responders. An AI agent detects events and identifies their locations. It generates occlusion zones and updates map data for route adjustments.
AI, dynamic navigation, event detection, occlusion zone
Patent
Adaptive ai coworker for organizational operations
Patent published on the 2026-05-07 in US under Ref US20260127021 by LUMINADATA INC [US] (Chafekar Deepti [us], Ara Afrozy [us])
Abstract: [0000] Examples relate to an adaptive AI coworker system for enhancing organizational operations. The system generates personalized AI coworkers based on role requirements, employing adaptive learning to understand unique organizational practices. It utilizes multi-agent coordination for complex task execution, automatically generating, prioritizing, and allocating tasks based on organizational context. The system integrates data from various sources, implementing data governance measures. Custo[...]
Our summary: The system generates personalized AI coworkers tailored to role requirements. It employs adaptive learning and multi-agent coordination for efficient task execution. Explainable AI features ensure transparency and compliance with data governance standards.
adaptive AI, organizational operations, multi-agent coordination, explainable AI
Patent
agentic AI and the future of electron microscopy
Published on 2026-04-10 by Vida Jamali, Amirali Aghazadeh, Josh Kacher @NATURE npj
Abstract: npj Computational Materials, Published online: 10 April 2026; doi:10.1038/s41524-026-02077-yAdvances in microscopy have long focused on improving resolution, throughput, and automation. The next transformation may lie in enabling microscopes to contribute to the reasoning that guides experiments. Recent advances in agentic artificial intelligence (AI) suggest a future in which microscopes do more than simply acquire images. Agentic systems could draw on prior knowledge, interpret experimental ou[...]
Our summary: Advances in agentic AI could enable electron microscopes to interpret data and design experiments. This transformation may shift microscopes from passive tools to active collaborators in research. The transition requires community support through open access and data sharing initiatives.
agentic AI, electron microscopy, experimental design, materials characterization
Publication
A Scoping Review
Published on 2026-02-01 by Jonathan Gibson, Praveen Chinniah, Shashank Chapala, Ojasvi Vemuri, Rajesh Botchu @MDPI
Abstract: Objectives: Artificial intelligence (AI) is a transformative development in the field of medicine. In the field of musculoskeletal radiology, agentic AI is a technology that could flourish, but currently, the limited evidence base is fragmented and sparse, and we present a scoping review of it. Methods: Parallel searches were conducted in four databases: PubMed, Embase, Scopus, and Web of Science. Search terms included all agentic AI and autonomous AI agents, as well as radiology. All papers und[...]
Our summary: This scoping review evaluates the potential of agentic AI in musculoskeletal radiology. It identifies eleven relevant studies highlighting improved decision support, workflow optimization, and image analysis. Despite promising findings, the evidence base remains limited and theoretical.
AI in Radiology, Musculoskeletal Imaging, Workflow Optimization, Decision Support
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