
Agentic AI designates AI systems that pursue multi-step goals autonomously by iterating through perception, planning, tool use, and self-correction loops without requiring human intervention at each decision point — a structural departure from single-turn prompt-response models toward architectures that decompose complex objectives into subtasks, execute actions against external environments, evaluate outcomes, and revise plans accordingly.
The canonical agentic architecture couples a large language model reasoning core with a tool registry — web browsers, code interpreters, API clients, file systems, database interfaces — and a memory architecture spanning short-term working context, episodic records of past actions, and retrieved long-term knowledge, allowing the agent to maintain coherent goal pursuit across extended interaction horizons that exceed any single context window. Multi-agent configurations extend this further, distributing subtasks across specialized agents coordinated by an orchestrator, introducing inter-agent communication protocols, result aggregation, and conflict resolution as additional architectural concerns.
The publications and patents indexed below address planning algorithms, tool use architectures, memory systems, multi-agent coordination protocols, agent evaluation benchmarks, and safety constraint methodologies.
This is our latest selection of worldwide publications and patents in english on Agentic AI, between many scientific online journals, classified and focused on agentic AI, AI agent, autonomous AI agent, multi-agent system, AI agent orchestration, agent planning loop, ReAct agent, chain-of-thought planning, tool-using AI agent, function calling AI, AI agent memory, long-term agent memory, episodic agent memory, agent working memory, AI agent task decomposition, hierarchical agent planning, AI agent self-correction, AI agent reflection, AI agent evaluation, agent reward shaping, AI agent sandboxing, agent safety guardrails, multi-agent coordination, agent communication protocol, AI agent tool registry, code-executing agent, web-browsing agent, retrieval-augmented agent, agent benchmark evaluation and human-in-the-loop agent.
Self-pruning fractal computational architecture for high-performance computing on resource-constrained and noisy quantum hardware
Patent published on the 2026-07-02 in WO under Ref WO2026139942 by MARECHAL THIERRY [CA] (Marechal Thierry [ca])
Abstract: A computational architecture employing self-pruning fractal branch management for achieving supercomputer-class performance on standard hardware and noisy intermediate-scale quantum (NISQ) devices. Unlike conventional parallel computing systems requiring massive hardware resources or genetic algorithms requiring extensive population evolution, this invention utilizes hierarchical fractal doubles—modular computational units organized in self-similar tree structures—with real-time adaptive pru[...]
Our summary: The architecture employs self-pruning fractal branch management for high-performance computing on noisy quantum hardware. It achieves supercomputer-class performance with modular units organized in fractal structures, utilizing real-time adaptive pruning to eliminate non-promising branches. Applications span neural architecture search, protein folding, and quantum system modeling, achieving significant speedups while being energy-efficient and noise-tolerant.
fractal architecture, quantum computing, self-pruning, computational efficiency
Patent
Hardware-enforced agentic genai workflow orchestrator with cryptographic ethical guardrails and human-in-the-loop escalation for autonomous clinical o
Patent published on the 2026-07-02 in US under Ref US20260188502 by BICKERSTAFF III GEORGE WILLIAM [US] (Bickerstaff Iii George William [us])
Abstract: A hardware-anchored orchestration system for autonomous GenAI agents in clinical settings, implementable in ASIC or FPGA fabric to ensure deterministic enforcement independent of software execution layers. The system utilizes a hardware-isolated ethical supervisor—comprising a HSM or TPM—to monitor agentic workflows against human-configured safety thresholds stored in an ethical guardrail manifest in a silicon vault. Hardware-based logic gates detect statistically anomalous token-level entro[...]
Our summary: The system orchestrates autonomous GenAI agents in clinical environments using hardware for deterministic enforcement. It employs a hardware-isolated ethical supervisor to monitor workflows against safety thresholds. In case of a safety breach, it activates a hardwired interlock to prevent non-compliant outputs.
Hardware Orchestration, GenAI Agents, Ethical Supervision, Cryptographic Compliance
Patent
Deterministic ai agent liability firewall and insurance engine with hardware-enforced envelope binding and privacy-preserving risk pooling
Patent published on the 2026-07-02 in US under Ref US20260187731 by BICKERSTAFF III GEORGE WILLIAM [US] (Bickerstaff Iii George William [us])
Abstract: A hardware-enforced AI liability containment system binds autonomous AI agent decisions to predefined liability envelopes retrieved from TEE-sealed policy stores within trusted execution environments (TEEs) comprising Intel SGX enclaves, AMD SEV-SNP protected VMs, or ARM TrustZone secure worlds, materially altering processor states to isolate all liability computations. Real-time risk exposure is computed using trust-state signals derived from TEE-resident hardware mechanisms comprising enclave-[...]
Our summary: This system binds AI agent decisions to liability envelopes using hardware-enforced mechanisms. It computes real-time risk exposure through TEE-derived trust-state signals. Insurance pools manage excess exposure with zk-SNARK proofs, ensuring verifiable operations in secure environments.
deterministic AI, liability containment, trusted execution environments, zero-knowledge proofs
Patent
Cross-agent context management for multi-agent system
Patent published on the 2026-07-02 in US under Ref US20260186828 by MICROSOFT TECH LICENSING LLC [US] (Wang Jeffrey [us], Katarya Vivek [us], Chandla Amol [us], Ng Christopher [us], Racca David Nicolas [it], Baruch Keren [us], Bottaro Juan Pablo [es], Sachindran Santhosh [us], Mohanasundaram Gokulraj [us], Arcara Kevin [us])
Abstract: An example maps input to a first task executable by a first task agent of a multi-agent application system. A search is formulated using the input and the first task. The search is executed on a second layer of a multi-layer memory accessible to the orchestrator agent. First cross-agent context data related to the input and the first task is extracted from results of the search. The first task and the first cross-agent context data are routed to the first task agent. Output is received from the [...]
Our summary: This content describes a method for managing context in a multi-agent system. It involves mapping input to tasks executed by agents and utilizing a multi-layer memory for searches. The process includes routing data between agents to generate responses based on task execution.
context management, multi-agent system, task execution, orchestrator agent
Patent
Information gathering using an intake artificial intelligence agent
Patent published on the 2026-07-02 in US under Ref US20260187111 by MAPLEBEAR INC [US] (Vrabec Helena Ursic [us], Bernard Benjamin [us], Lei Kevin [us], Cohen Spencer Lee [us], Lee Junghoon [ca], Bowering Robert [ca], Hohenberger Taylor [us])
Abstract: [0000] A question is posed by a user of a user device as part of an online chat session with an online system. An intake artificial intelligence (AI) agent interacts with the user via the online chat session in one or more rounds of messaging to gather information that may be used by a human agent to respond to the question. At some point, the online system may identify in an output of the intake AI agent an indication that there is sufficient context regarding the question to transfer the quest[...]
Our summary: An intake AI agent interacts with users in an online chat to gather relevant information. The system assesses when enough context is available to transfer the interaction to a human agent. The human agent utilizes the gathered session information to formulate a response to the user s question.
AI agent, information gathering, online chat, human agent
Patent
Knowledge extraction system and knowledge extraction method
Patent published on the 2026-07-02 in US under Ref US20260187494 by HITACHI LTD [JP] (Suzuki Shintaro [jp], Hyodo Akihiko [jp], Sakaniwa Hidenori [jp])
Abstract: [0000] An AI agent system includes an episode memory database configured to accumulate dialogue histories with users as episode memories including a plurality of messages—, a semantic memory construction unit configured to cluster the messages included in the episode memories into a plurality of clusters and extract scenario branches based on transitions of the messages between the clusters, and a semantic memory database configured to store the scenario branches as semantic memories.[...]
Our summary: The AI agent system accumulates dialogue histories in an episode memory database. It clusters messages into groups and extracts scenario branches based on message transitions. The scenario branches are stored as semantic memories in a dedicated database.
knowledge extraction, AI agent, semantic memory, dialogue history
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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