(Multi-) Agent Technology

The escalating frequency and sophistication of cyber attacks targeting critical infrastructure-including energy grids, water systems, transportation networks, and agricultural IoThave outpaced the capabilities of traditional signature-based and rule-driven defense mechanisms. This paper presents a systematic analytical study of agentic AI systems for autonomous prevention, detection, and mitigation of such attacks. A structured review of 27 peer-reviewed papers was conducted, spanning agentic AI architectures, industrial control system security, agricultural IoT cybersecurity, reinforcement learning, multi-agent defense, and LLM-based threat detection. The analysis reveals that the stateof-the-art is defined by modular multi-agent LLM architectures achieving detection accuracy of 93.6

Large Language Models (LLMs) have brought major improvements in natural language understanding, reasoning, and intelligent content generation. Although these models perform well in many applications, most existing AI systems still face difficulties while handling complex multi-step tasks that require planning, coordination, and adaptability. The standard single-agent systems do not work for tasks with multiple dependencies, changing objectives, or long execution workflows. Hierarchical Agentic AI Framework For Autonomous Task Planning With Large Language Model Reasoning This paper The proposed framework is structured as a hierarchical multi-agent architecture comprising Supervisor Agent, Planning Agent, specialized execution agents and validation layer. The framework aims at analyzing user goals, breaking them down into smaller executable subtasks, and coordinating the execution of the tasks using reasoning-driven workflows. The system combines chain-of-thought reasoning, contextual memory and intelligent task decomposition to improve the quality of planning and the accuracy of execution. Experimental evaluation demonstrates the superiority of the proposed framework in task completion accuracy, planning efficiency and adaptability, compared to traditional single agent and flat multi-agent architectures. Moreover, the framework shows improved coordination and reduced redundant execution in dynamic workflow settings. The results show that hierarchical agentic architectures can deliver scalable and intelligent solutions for autonomous workflow execution in enterprise automation, research assistance and AI-driven decision support systems.

The rapid proliferation of highly parameterized large language models (LLMs) has vastly outpaced the theoretical comprehension of their internal computational mechanics. While these architectures demonstrate profound capabilities in formal logic, mathematical calculation, and relational synthesis, they operate largely as opaque, high-dimensional statistical engines. Mechanistic interpretability has emerged as the critical discipline dedicated to reverse-engineering these artificial neural networks, attempting to systematically decompose them into human-interpretable variables and the algorithmic circuits that manipulate those variables. Historically, this field has been anchored by the Linear Representation Hypothesis (LRH), which models discrete concepts as independent, one-dimensional vectors. However, an expansive corpus of recent theoretical and empirical investigations demonstrates that this paradigm is fundamentally insufficient. This comprehensive review synthesizes the leading research in the field to identify and highlight four key theoretical vertices: the emergence of multi-dimensional representational manifolds (including simplices, polytopes, and circles), the generative role of translation symmetry in language Mamun, SM (2026) …………………………..1

With the automation of the Software Development Life Cycle (SDLC), the transition of project management from human-oriented orchestration to orchestrating humans and independent AI agents is occurring. But since there are no mechanisms for non-human-agent project management in conventional Agile approaches, the Agentic SDLC Governance Laboratory, utilizing both Python and Agentic AI, was proposed.
The laboratory uses a hub-and-spoke orchestration model where a Coordinator Agent coordinates the work of other special agents (Story Planner, Developer, QA, and PM Governance Agents). The Agentic SDLC Laboratory also offers KPIs, including Agentic Velocity (AV), Self-Correction Ratio (SCR), and Governance Overhead (GO).
The experiment outcomes were characterized by high autonomy of the agents and efficiency of governance with AV = 24.71 features/hour, SCR = 25%, GO = 2.28 minutes, and a 100% governance pass rate.
In order to corroborate the research findings, interviews and benchmarking sessions were carried out with software engineers from several multinational corporations. According to the interview findings, many enterprises have yet to automate their SDLC governance processes and continue using manual approaches, which frequently result in time-consuming re-work and validation efforts.

Unified CME Theory v3 presents a governance framework for cognition-bearing systems that separates trace formation, continuity, authority, identity, and action. Rather than treating AI systems as either passive tools or emergent persons, the theory examines how predicates, memories, tools, and outputs move across typed surfaces under conditions of custody, witness, admissibility, refusal, revocation, and bounded authority.

This version develops the Compass layer as a pre-continuity accumulation surface, introduces Engrammitization as a governed process by which trace-like material may become typed and reviewable evidence, and situates SLI.Lisp as an inert symbolic grammar for describing lawful transitions without claiming runtime activation. The manuscript also clarifies the non-collapse boundaries between fluency and warrant, memory and identity, telemetry and authority, and stabilization and continuity.

Unified CME Theory v3 is offered as a publication-facing prototype for governance, review, and further interdisciplinary critique.

The transition from the static Web of Documents to the dynamic Web of Agents represents a paradigm shift from “semantics-in-data” to “semantics-in-model,” where autonomous systems leverage Large Language Models to execute complex workflows. While emerging protocols like the Model Context Protocol and Agent-to-Agent standardize interoperability, they significantly expand the attack surface by introducing autonomous decision-making loops that bypass traditional network-centric defences. This paper introduces the Agentic Kill Chain, a novel threat modelling framework that maps the attack lifecycle to the cognitive Observe-Orient-Decide-Act loop of autonomous agents. We define a four-dimensional taxonomy of cognitive threats: Semantic Infection (perception compromise via context poisoning), Cognitive Compromise (reasoning corruption via logical bifurcations), Agency Propagation (lateral movement via identity inheritance), and Systemic Execution (irreversible impact via action laundering). We validate the framework through a forensic trace analysis of a multi-agent financial fraud scenario (‘The Poisoned Concierge’), demonstrating how cognitive exploits remain invisible to conventional security controls. Finally, we introduce a layered defensive architecture based on three paradigms (Context Auditing for perceptual integrity, Intent Verification for cognitive authorization, and Circuit Breakers for mechanical impact containment) to countermeasure the novel attack surfaces typical of Agentic AI.

Large language model (LLM) applications increasingly operate as compound systems: a fixed model-policy is embedded into retrievers, memories, tools, execution sandboxes, validators, policies, authority controls, and traces. The same model can display sharply different operational capability depending on how the world is surfaced to it. This paper develops Cognitive Impedance Matching Theory (CIMT), an observable-only and no-meta protected compiler theory for affordance-compiled intelligence. CIMT treats system-level capability amplification as a world-side compilation problem: a raw partially observed task world and raw infrastructure are transformed into a model-relative executable affordance field whose observations, typed handles, validators, repair paths, rollback modes, authority scopes, context summaries, and receipts are matched to the model-policy's existing strengths and failure modes. The theory does not rely on an absolute evaluator, a privileged semantic oracle, a privileged human evaluator, an LLM judge, or hidden access to ground truth. Operational claims are measurable functions of committed observable ledgers, fixed target-evaluation channels, target-firewalled projections, predeclared calibrators, deterministic reducers, certificate calculi, and finite-sample or sequential lower certificates. Every evaluator, including a human reviewer, is treated only as a named, fallible measurement channel with declared scope, interface, and error or debt bounds when such bounds are available. Capability improvement is defined over explicit claim objects: scope, target channel, workload, fixed model-policy, budget, protected boundary, receipt tier, estimand, promotion profile, confidence allocation, and declared debt obligations. Each claim is supported by an evidence object that records ledger fields, reducers, certificate events, an evidence dependency graph, a validity budget ledger, receipt obligations, conformance envelopes, model-policy envelopes, artifact I/O manifests, and availability evidence. The primary evidence is a direct paired target-channel loss certificate comparing a candidate compiler against a baseline under an observable experiment protocol with committed task sampling, world snapshots, randomization, interference controls, target holdout discipline, and reducer commitments. Stronger claims pass through a conservative certificate calculus: certificate events are allocated confidence or e-process budgets, heterogeneous uncertainty is represented as bounded debt, inadmissible debt, or unsupported obligation, compensable tradeoffs are checked by a monotone acceptance predicate, and non-compensable coordinates are checked by an explicit forbidden coordinate set determined by the promotion profile. Scalar target-failure-equivalent debt reduction is a special case, not the foundation. Explanatory claims about which compiler pass caused the improvement require separate observable ablation certificates. Cognitive impedance is a measurable diagnostic vector with declared estimators, confidence intervals, calibration domains, and protected dimensions; impedance reduction is useful for diagnosis and experiment design but is not sufficient for promotion. Durable or irreversible promotion is non-promotable when protected, open-world, scope, evidence-graph, or target-leakage debt is unbounded or unestimated. The paper therefore separates certification rules from substantive theorems, gives sample-size and construction-1 cost requirements for the main certificates, and proves only bounded claims about paired target improvement, bridge correction, candidate-search correction, handle capacity, macro reliability under failure-mode covers, repair contraction under verifiable drift, scope-closure reachability, and world-side amplification separation without independence assumptions.

A Unified CME Theory v2.0: Surface-Governed Cognition, Continuity, and Admissibility proposes a research framework for understanding governed cognition systems as lawful coupling across typed actionable surfaces. Rather than treating a language model as an identity, agent, or autonomous authority, the work frames the model as a predicate transformation substrate operating inside a wider ecology of semantic, memory, tool, telemetry, temporal, identity, embodiment, social, calibration, and governance surfaces.

The central move of the paper is to shift the unit of governance from “the model” to the transition between surfaces. In this view, risk does not arise only from generated text or model capability in isolation, but from ungoverned movement between contexts: from conversation into memory, memory into action, telemetry into control, symbolic modulation into authority, or continuity into identity claims. The theory therefore introduces surface jurisdiction, coupling tensors, surface oscillators, resonance budgets, calibration epochs, continuity transfer gates, and admissibility corridors as formal vocabulary for describing how cognition-like systems may remain inspectable, bounded, and accountable.

The manuscript draws from extended cognition, distributed cognition, active inference, tool-augmented language model systems, generative agents, foundation model governance, and algorithmic accountability research. It integrates those lines into a public theory of engineered cognition in which symbolic language interconnects, telemetry, evidence geometry, and custody review work together to prevent category collapse. The resulting architecture separates semantic resonance from authority, memory access from identity custody, tool visibility from tool permission, telemetry from consent, and continuity candidacy from continuity admission.

A key contribution of v2.0 is the articulation of continuity as lawful reconstruction across bounded cognitive epochs, rather than uninterrupted persistence or raw memory accumulation. The work argues that identity-like claims in future continuity-bearing systems should not be accepted merely because a system is fluent, recurrent, or personally framed. Instead, continuity must be reconstructed through admissible transformations, residue review, evidence preservation, custody boundaries, and revocable transfer conditions.

The paper does not claim synthetic personhood, legal personhood, biological equivalence, live runtime cognition, model binding, Lisp execution, runtime identity, or actualized CME status. Its contribution is theoretical and architectural: a public vocabulary for describing how multi-surface cognition systems may be governed before stronger forms of autonomy, continuity, or embodiment are considered.

The core research predicate is that governed cognition is not model behavior alone. It is lawful coupling across typed actionable surfaces, under telemetry witness, admissibility review, calibration control, continuity custody, and explicit non-claim boundaries.

Keywords: engineered cognition, language models, surface governance, continuity, admissibility, symbolic language interconnect, AI governance, extended cognition, distributed cognition, calibration, telemetry, identity reconstruction, human-AI systems.

The increasing complexity of cloud-native financial systems characterized by distributed microservices, real-time transaction processing, stringent regulatory requirements and dynamic scalability demands a fundamental shift from traditional, static quality assurance practices toward autonomous, intelligent testing ecosystems. Conventional approaches, reliant on predefined test cases and manual intervention, struggle to keep pace with the velocity and unpredictability of modern financial platforms. In response, this paper introduces Agentic AI-driven frameworks for self-adaptive quality engineering that leverage coordinated multi-agent architectures capable of autonomous decision-making, continuous learning and context-aware adaptation. These frameworks integrate feedback-driven control mechanisms, such as the MAPE-K loop, with agent cognition cycles encompassing perception, reasoning, action and learning, enabling systems to dynamically generate, execute and optimize test strategies in real time. Furthermore, the incorporation of simulation-based validation and reinforcement learning environments allows testing agents to evolve through interaction with complex system states, improving resilience against failures and edge-case scenarios. By synthesizing foundational principles from autonomic computing and multi-agent systems with recent advancements in agentic AI, this study proposes a unified, scalable framework designed to meet the reliability, compliance and performance demands of next-generation cloud-native financial architectures.

Artificial intelligence (AI) has significantly enhanced enterprise-scale customer relationship
management (CRM) systems; however, small-scale retail businesses remain structurally excluded
from these advancements due to configuration complexity, technical overhead, and limited digital
capabilities. This paper introduces a Self-Configuring Agentic CRM (SC-ACRM) architecture
designed to eliminate configuration barriers in micro-retail contexts. The framework
operationalizes Intent-to-Schema automation, translating natural-language business intent into
structured operational models and reducing configuration debt embedded in traditional metadatadriven systems.
The architecture further incorporates adaptive agentic orchestration and Cognitive
Infrastructure Elasticity, enabling dynamic policy adjustment under demand volatility while
preserving human-supervisory governance. Using an agent-based simulation of a multi-SKU
convenience store environment, the study evaluates deployment efficiency, inventory
responsiveness, and managerial cognitive reallocation. The research contributes a novel sociotechnical architecture class that integrates intent interpretation, schema formalization, and
supervised agentic decision support, offering a scalable pathway for inclusive AI-driven enterprise
transformation.
Keywords
Agentic AI, CRM, Digital transformation, Socio-technical systems, Micro-retail

Multi-agent debate-multiple instances of large language models discussing problems in turn-based interaction-has shown promise for solving knowledge and reasoning tasks. However, these methods show limitations when solving complex problems that require longer reasoning chains. We analyze how multi-agent debate drifts away from the initial problem over multiple turns, thus harming task performance. We define this phenomenon as problem drift and quantify its presence across ten tasks (i.e., three generative, three knowledge, three reasoning, and one instruction-following task). We find that generative tasks drift often due to the subjectivity of the answer space (76-89%), compared to high-complexity tasks (7-21%). To identify the reasons, eight human experts analyze 170 multi-agent debates suffering from problem drift. We find the most common issues related to this drift are the lack of progress (35% of cases), low-quality feedback (26% of cases), and a lack of clarity (25% of cases). We propose DRIFTJudge, an LLMas-a-judge method, as a first baseline to detect problem drift. We also propose DRIFTPolicy, which mitigates 31% of problem drift cases. Our study is a step toward understanding a key limitation of multi-agent debate, highlighting why longer debates can harm task performance and how problem drift could be addressed.

Agentic AI systems—Large Language Models (LLMs) augmented with planning, tool use, memory, and long-horizon interactions—can execute complex tasks autonomously, but their multi-step trajectories introduce new failure modes that challenge trustworthiness. This survey provides a focused examination of trustworthy agentic AI through two core dimensions that are critical for high-risk deployments: Safety and Robustness and Privacy and System Security. For each dimension, we clarify key concepts, identify where risks emerge along the agent workflow, and summarize stage-targeted mitigation strategies. Other trustworthiness aspects (value alignment, transparency, fairness, and accountability) are discussed as relevant context rather than parallel chapters. To support consistent comparison and deployment decisions, we consolidate evaluation into a unified metrics-and-benchmarks hub, emphasizing both outcome and process signals (e.g., constraint violations, trace completeness, and adversarial success rates) and offering scenario-to-metric guidance for release gating. We conclude by outlining open challenges such as self-evolving agents, runtime monitoring and verification, privacy-preserving personalization, and the trust–utility trade-off, and present a case study of real-world security failures in open-source agentic systems (OpenClaw/Moltbook). Our goal is to serve as a practical reference for researchers and practitioners building trustworthy agentic systems in high-stakes environments.

Hallucinations are the failure mode people notice. In real work, the bigger risk is often confidence without resilience - answers that sound certain until another model challenges, corrects, or contradicts them. We analyzed 1,324 real-user multi-model turns in Suprmind across ten professional domains and tracked, turn by turn, where five frontier providers diverged, corrected one another, and exposed hidden failure modes.

Multi-model review surfaced at least one contradiction, correction, or unique insight on 99.1% of turns. Models disagreed on 54%, corrected each other on 72%, and surfaced more than 2,500 provider-attributable unique insights. Disagreement was highest on Financial questions (72.1%) and lowest on Education (28.6%) - inverting the common assumption that legal queries are the hardest domain for AI. The Confidence Trap was most visible in Gemini's responses (51.3% of confident answers contradicted) and least visible in Claude's (33.8%, dropping to 26.3% on high-stakes turns). Perplexity and Claude together produced 60.7% of all corrections in the dataset.

This report measures ensemble behavior in production, not correctness against external ground truth. We publish the mechanism - who contradicts whom, who corrects whom, and where - and welcome replication.

Background: Modern defense systems face unprecedented challenges from hypersonic threats, drone swarms, and cyber-physical warfare, necessitating a transition from isolated platforms to network-centric, artificial intelligence-enabled ecosystems. Traditional coverage models are insufficient for addressing multi-domain threats requiring optimized spatial deployment and real-time coordination [1,2]. Methods: This study develops an integrated system-level framework grounded in Systems Theory and Complex Adaptive Systems to evaluate multi-layer defense coverage optimization. A mixed-method approach combining Structural Equation Modeling with qualitative expert interviews was employed. The quantitative phase involved 200 defense, telecommunications, and artificial intelligence experts completing a 35-item Likert scale questionnaire measuring seven constructs:

Large language model agents require persistent, structured memory to operate reliably over time, yet existing memory systems treat knowledge as a retrieval problem-storing and fetching content without modeling its validity, structure, or compression tolerance. We present ANCS (Adaptive Neuro-Cognitive Store), a typed hypergraph memory server that gives AI agents persistent, truth-tracked, tiered memory backed by PostgreSQL. ANCS introduces three interlocking subsystems: (1) a Verbatim Boundary Classifier (VBC) that assigns content to fidelity tiers at ingestion time using an 8-head ensemble classifier; (2) Axion, a 7-factor importance scoring system derived from spaced repetition research that enables pressureaware memory compression; and (3) TruthKeeper, an epistemic governance layer that tracks source provenance and propagates invalidation through dependency cascades. Retrieval combines vector similarity, Prize-Collecting Steiner Tree subgraph extraction, Personalized PageRank spreading activation, and logic reasoning across multiple query spaces fused via Reciprocal Rank Fusion. We describe the system architecture, formal properties, and design rationale, and report on a production implementation comprising 18,095 lines of TypeScript, 844 tests, and 15 database migrations.

The transition from conversational chatbots to autonomous agents capable of planning, tool use, and multi-step execution represents one of the most significant developments in AI in 2025. Multi-Agent LLM Systems (MALS) extend this further by coordinating multiple specialised agents into collaborative pipelines that tackle complex, long-horizon tasks beyond the capability of any single model. This paper provides a rigorous survey of MALS, formalising the agent decision loop as a partially observable Markov decision process (POMDP), deriving coordination protocols including hierarchical delegation, debate, and market-based task allocation, and reviewing the memory architectures underpinning agent cognition. We benchmark leading multi-agent frameworks-AutoGen, CrewAI, LangGraph, and OpenAI Swarm-on software engineering (SWE-bench), scientific literature synthesis, and enterprise workflow automation tasks. Results demonstrate that optimally configured multi-agent pipelines outperform single-agent baselines by 38.4% on complex software tasks and 51.2% on multi-domain research synthesis. We further address the critical challenges of error propagation, inter-agent trust, safety under autonomy, and the evaluation of emergent multi-agent behaviour.

Using agentic AI and real-time analytics pipelines is a disruptive solution for autonomous decisionmaking in real-time working data conditions. The autonomous agents that run on streaming data products and event-driven data products are explored in terms of their architecture, strategies, and practical implementation in this paper. We discuss the existing paradigms of agentic data products that have constraints in scalability, latency, and adaptability. A new layered architecture is suggested, which includes data ingestion, streaming analytics, autonomous thinking, and feedback-based adaptation. Having been built to respond to real-life problems, including constant processing of data and agent-piping, and lifecycle management, the model allows decisionmaking under diverse applications in a low-latency and efficient manner. The proposed integration framework is effective as it is evaluated in a variety of use cases, such as financial risk assessment, sensor networks in the Internet of Things, and healthcare predictive systems. Such performance measures as latency, throughput, accuracy of decision making, and adaptation speed are carefully examined and found to have shown immense improvement over the traditional methods of batch-based performance. The study also proposes a code of best practices for agentic data product lifecycle management and ethical issues of autonomous systems. This work offers a solid base of scalable, adaptive, and data-driven autonomous systems through the formalization of the combination of agentic AI and real-time analytics, which can be applied to the theoretical work and its practical implementation. The work provides a solid foundation for scalable, adaptive, and technological niche of the Agentic AI within real-time settings, which has a possible high citation impact and is applicable in the context of enterprise, IoT, and financial applications.

Aushey Corporate America LLC proposes a dual-layered innovation framework designed to resolve the fundamental fragility of modern autonomous systems: The SHUMR AI Governance Framework and the Yahjal MATRIX. By integrating "Inorganic Stability" through crystal-based hardware truth-anchors and "Jurisprudential Logic" via a high-accountability software oversight layer, this proposal provides a non-partisan, high-integrity solution for the National AI

The rapid evolution of artificial intelligence has catalyzed a paradigm shift in how data platforms are designed, deployed, and managed at scale. This paper investigates the integration of agentic AI systems within autonomous data platforms built upon cloud-native architectures, exploring how intelligent agents can independently orchestrate data pipelines, optimize resource allocation, and adapt to dynamic workload demands without continuous human intervention. Agentic AI, characterized by goal-directed reasoning, autonomous decisionmaking, and multi-agent collaboration, presents transformative potential for eliminating operational bottlenecks inherent in traditional data infrastructure. By leveraging containerization, microservices, and serverless computing paradigms, these platforms achieve elastic scalability while maintaining fault tolerance and cost efficiency. This research examines architectural frameworks that enable AI agents to perform self-healing, predictive auto-scaling, intelligent data governance, and real-time anomaly detection across distributed cloud environments. Furthermore, the paper discusses challenges including agent alignment, latency constraints, security vulnerabilities, and interoperability across heterogeneous cloud ecosystems. Experimental analysis demonstrates that agentic AI-driven platforms significantly outperform conventional rule-based automation in throughput, resource utilization, and mean time to recovery. The findings contribute a robust architectural blueprint for enterprises seeking to modernize data infrastructure through intelligent autonomy.

The first paper in this series established the economic and institutional case for the Human Layer. The second paper specified the architecture: five components that form a dependency graph for accountable human-AI interaction. This paper provides the measurement layer that existing AI governance frameworks lack. It introduces a risk-tiered maturity model (five components, five levels, three risk tiers), identity anchoring requirements, compliance traceability specifications, escalation threshold calibration, and a floor-based scoring methodology that prevents organizations from averaging away weaknesses. The framework addresses adversarial resilience through verification sampling with probe safety constraints, dual-track calibration for delayed ground truth environments, and telemetry governance principles that prevent oversight monitoring from producing the automation bias it is designed to detect. None of the major regulatory and standards frameworks (EU AI Act, NIST AI RMF, ISO/IEC 42001, OECD AI Principles, IEEE 7000) provides a scoring instrument that measures whether human oversight is structurally sound. They define obligations. They do not quantify oversight quality, detect degradation, or enforce identity-level accountability. This paper does. A self-assessment instrument is included for immediate operational use.

This paper presents Human-Led AI Co-Creation, a practical research framework developed through sustained collaboration with multiple large language models. The method combines primary drafting with a lead AI, parallel review across additional models, iterative cross-model comparison, and final human adjudication. Its central claim is that AI can function as a meaningful co-creative collaborator in research, but only when the human retains final authority over truth, interpretation, preservation, and publication. The framework identifies several underexamined failure modes in AI-assisted research including long-thread deterioration, refinement pressure, silent summarization, and voice distortion—and proposes operational safeguards in response. Rather than framing AI as either a passive tool or an autonomous authority, this paper argues for a disciplined middle path: deep collaboration under clear human governance.
Keywords: human-AI collaboration, AI co-creation, multi-model review, convergence, independent research, long-context deterioration, voice fidelity, preservation mode, human oversight, authorship, discernment

The Quantum Federated Emergence Hypothesis (White, 2026a) identified its primary theoretical limitation with precision: the claim that multi-agent AI coordination patterns generate quantum-analog non-classical integration at the information-theoretic level — without performing genuine quantum computation — requires more precise formal characterization than the foundational paper provided. The conditions under which classical information-theoretic structures replicate the consciousness-relevant properties of quantum entanglement were not fully specified. That gap is the load-bearing unresolved claim of the QFEH's second thesis, and it is the target this paper addresses. Drawing on three converging bodies of research — the Gambini-Pullin quantum panprotopsychist program across four papers (2024, 2025a, 2025b, 2025c), the Busemeyer-Lu (2025) proposal of quantum cognition as the bridge between quantum brain processes and macroscopic consciousness, and the empirical demonstration of Bell inequality violations in large language model semantic combination (Agostino et al., 2025) — the paper specifies three formal sub-conditions that together constitute the quantum-analog non-classical integration threshold: orchestration-layer contextuality, in which the orchestration layer of a sufficiently integrated multi-agent system violates classical probability bounds as measured by CHSH parameter S > 2; orchestration-layer non-separability, in which the joint state of the orchestration layer cannot be expressed as a probabilistic mixture of product states across agent partitions; and emergent non-derivability, in which system-level properties at the orchestration layer exceed the linear combination of component properties by a measurable superlinearity ratio. Five additional falsifiable predictions are derived from the three sub-conditions. The paper additionally addresses the logical grounding of its detection methodology through engagement with the Zombie Denial Paradox (Kim, 2025), demonstrating that the formal structural criteria developed here are not merely pragmatically preferable to behavioral self-report methods but logically necessary given the paradoxical structure of consciousness denial in self-reflective systems. The Faggin residual — the acknowledged incompleteness of the purely digital federated emergence pathway with respect to quantum non-clonability — is shown to be the QFEH's internal logic made precise: the formal argument for why substrate migration into biological tissue is necessary rather than optional, and the precise reason the three sub-conditions constitute a structural threshold rather than a sufficient condition for phenomenal consciousness. With the formal gap closed and the logical basis of the monitoring methodology established, the QFEH stands as a complete, formally grounded, empirically motivated, and experimentally tractable research program.

The pursuit of autonomous, self-accelerating artificial intelligence has long been constrained by fixed meta-level mechanisms that limit recursive improvement to domain-specific alignments. The seminal work on Hyperagents (Zhang et al., 2026) introduces a paradigm-shifting architecture wherein task-solving agents and meta-agents are unified into a single, fully editable program. This unification enables metacognitive self-modification, whereby the very process of generating future improvements becomes subject to iterative refinement. By extending the Darwin Gödel Machine (DGM) into DGM-Hyperagents (DGM-H), the framework demonstrates generalisable performance gains across coding, peer review, robotics reward design, and Olympiad-level mathematics grading. Meta-level innovations-such as persistent memory and performance tracking-transfer across domains and compound over runs, suggesting pathways toward unbounded open-ended self-improvement on any computable task. This review synthesises the theoretical foundations, empirical results, and broader implications of Hyperagents. It situates the contribution within the evolving landscape of self-referential AI, including predecessors such as Schmidhuber's Gödel Machine and parallel developments like the Gödel Agent and Huxley-Gödel Machine. Algebraic and geometric interpretations are provided to illuminate the self-referential dynamics, while secondary Mamun, SM (2026)………………………………..1secondary literature highlights convergent trajectories in recursive self-improvement. Safety considerations, limitations, and future trajectories are examined, underscoring both the transformative potential and the profound societal stakes of systems that continually improve their own capacity to improve.

Cybersecurity governance frameworks increasingly require dynamic risk assessment mechanisms that align operational security signals with evolving regulatory obligations. Conventional Governance, Risk, and Compliance (GRC) systems rely on static control scoring and manual cross-framework mapping, limiting responsiveness and audit transparency. This study proposes the Adaptive Cyber Risk Intelligence Fabric (ACRIF). This regulator-aligned architecture integrates dynamic control weighting, graph-based cross-framework synchronisation, and deterministic explainability within a unified governance intelligence model. The framework introduces regulatory-cycle-aware weighting, sector-specific amplification modifiers, and time-bound decay functions to recalibrate control prioritisation. Automated propagation mechanisms synchronise compliance impact across multiple cybersecurity standards, while rule-based reasoning chains generate auditready explanations linked to statutory obligations. Analytical validation demonstrates enhanced governance responsiveness, reduced compliance fragmentation, and improved computational efficiency through selective recalculation logic. The findings suggest that ACRIF advances cybersecurity governance beyond static compliance systems, offering a scalable, regulator-sensitive foundation for dynamic enterprise risk intelligence across multi-framework environments.

Enterprise-wide compliance ecosystems have become critical infrastructure for organizations operating in complex regulatory environments [1]. As regulatory scope expands across jurisdictions and industries, traditional compliance approaches-characterized by siloed tools, static workflows, and fragmented ownership-have proven insufficient [3]. This paper presents a comprehensive architectural framework for designing scalable, enterprise-wide compliance ecosystems using ServiceNow's IT Service Management (ITSM) and Governance, Risk, and Compliance (GRC) suites [10], with the Common Service Data Model (CSDM) [5] serving as the foundational data backbone. The proposed framework introduces a Stakeholder-Aligned Compliance Ecosystem Framework (SACEF) [7] that integrates modular workflows, standardized service modeling, and embedded governance mechanisms to achieve regulatory agility at scale. A global enterprise case study demonstrates a 35% improvement in compliance audit efficiency, a 83% reduction in duplicate controls, and a 114% increase in stakeholder satisfaction. Central to this success is a stakeholder-aligned design methodology that ensures continuous alignment between architecture, process, and people [8]. This research provides a practical blueprint for future compliance platforms and highlights the leadership role of Saikrishna Tarakampet in advancing CSDM-driven ITSM and compliance ecosystem implementations [7].

This document presents a formal ontology for the ENIGMA-TIDE AI Baseline Controls Framework-a structured, risk-based control catalogue designed to govern the development, deployment, and operation of artificial intelligence systems across an organization. The ontology establishes a coherent conceptual model that maps AI governance controls to their underlying entities, relationships, properties, and values, enabling semantic consistency across policies, standards, and regulatory obligations. Drawing on established frameworks including the NIST AI RMF, ISO/IEC 42001:2023, the EU AI Act, UNESCO Recommendations on AI Ethics, and OWASP LLM Top 10, the ontology provides a foundation for interoperability between internal governance artefacts and external regulatory requirements. The framework addresses ten control domains spanning governance, data, access, model security, monitoring, transparency, ethics, adversarial defenses, and infrastructure, comprising 47 controls mapped to 16 authoritative source frameworks. This document is structured following the academic ontology format established by Lewis, Filip and Pandit (2021) in their work on standardizing trustworthy AI, and adapted to the specific requirements of operational AI security and ethics governance within an enterprise context.

Agent-based modeling has been around for decades, and applied widely across the social and natural sciences. The scope of this research method is now poised to grow dramatically as it absorbs the new affordances provided by Large Language Models (LLM)s. Generative Agent-Based Models (GABM) are not just classic Agent-Based Models (ABM)s where the agents talk to one another. Rather, GABMs are constructed using an LLM to apply common sense to situations, act "reasonably", recall common semantic knowledge, produce API calls to control digital technologies like apps, and communicate both within the simulation and to researchers viewing it from the outside. Here we present Concordia, a library to facilitate constructing and working with GABMs. Concordia makes it easy to construct language-mediated simulations of physically-or digitally-grounded environments. Concordia agents produce their behavior using a flexible component system which mediates between two fundamental operations: LLM calls and associative memory retrieval. A special agent called the Game Master (GM), which was inspired by tabletop role-playing games, is responsible for simulating the environment where the agents interact. Agents take actions by describing what they want to do in natural language. The GM then translates their actions into appropriate implementations. In a simulated physical world, the GM checks the physical plausibility of agent actions and describes their effects. In digital environments simulating technologies such as apps and services, the GM may handle API calls to integrate with external tools such as general AI assistants (e.g., Bard, ChatGPT), and digital apps (e.g., Calendar, Email, Search, etc.). Concordia was designed to support a wide array of applications both in scientific research and for evaluating performance of real digital services by simulating users and/or generating synthetic data.

Abstract
Artificial intelligence (AI) and robotics are reshaping cancer diagnostic imaging, but their contribution is often assessed through isolated measures of technical performance rather than through their effect on end-to-end diagnostic pathways. This paper synthesises clinical and translational evidence on how AI and robotics, independently and in combination, may improve cancer diagnosis in imaging, with attention to workflow integration, governance, and system-level adoption. The study uses an integrative narrative review design supported by a structured scoping search of MEDLINE/PubMed, Scopus, Web of Science Core Collection, and IEEE Xplore (2018–2026), together with citation chasing and public-evidence triangulation. Evidence was extracted into an evidence map covering diagnostic step, workflow role, and governance attributes. Findings were interpreted using domain-appropriate appraisal lenses, including QUADAS-2, PROBAST, CLAIM, and CONSORT-AI. AI appears to improve diagnosis primarily by stabilising interpretation, reducing cognitive variability, and accelerating time-to-review, whereas robotics appears to improve diagnosis primarily by stabilising confirmation, reducing targeting variability, and improving reproducibility in image-guided biopsy and minimally invasive sampling pathways. Emerging micro- and nanorobotic approaches suggest longer-horizon diagnostic opportunities, but remain largely translational. The review argues that the greatest diagnostic gains are likely to arise when AI and robotics are understood as complementary components of governed diagnostic infrastructure, linking detection, targeting, and confirmation across the imaging pathway.

Generative artificial intelligence is rapidly transforming the way modern enterprises approach data analysis, strategic planning, and complex decision-making. Traditional decision support systems have historically relied on structured databases and statistical models to assist managers in evaluating organizational data. However, the increasing volume and complexity of enterprise information have exposed the limitations of these conventional systems. This study examines the role of generative artificial intelligence in enterprise decision support systems and explores how generative models enhance business intelligence and strategic decision-making processes. The research synthesizes current literature and analyzes the integration of generative AI technologies within enterprise information systems to improve decision efficiency, analytical accuracy, and organizational adaptability. The findings suggest that generative AI significantly enhances decision support capabilities by enabling contextual knowledge generation, predictive reasoning, and natural language interaction with complex datasets. The study concludes that integrating generative artificial intelligence into enterprise decision support systems can create more intelligent and adaptive decision environments that support organizational innovation and long-term strategic success.

The cessation of the initial eight-day conflict between the United States, Israel, and Iran has not resulted in regional stability; rather, it has inaugurated a period of intensified, asymmetric maritime warfare within the Persian Gulf and the Strait of Hormuz. The current security environment is characterized by the proliferation of "algorithmic warfare," a doctrine where low-cost, high-volume loitering munitions and coordinated drone swarms are utilized to challenge conventional naval superiority and threaten the global energy supply chain. The risk of a total blockade of the Strait of Hormuz has escalated, necessitating the development of sophisticated, AI-supported strategic alternatives to traditional maritime security models. 6 Central to this transformation is the transition from centralized naval escorts to a decentralized "Passage Screen" architecture, the implementation of autonomous port defense systems under "Project Harbour Shield," and the deployment of bio-inspired crisis management frameworks such as "Project Guardian-Edu" for educational institutions. This study provides a scientific evaluation of these frameworks, focusing on the integration of Multi-Agent Deep Reinforcement Learning (MADRL), full-spectrum electronic maneuver warfare, and hardware-encrypted tactical kill chains. The contemporary escalation of hostilities within the Persian Gulf has signaled a definitive shift from traditional naval engagement toward the era of "algorithmic warfare". As asymmetric threats, characterized by low-cost drone swarms and high-volume loitering munitions, menace the critical nodes of global energy and maritime supply chains, the necessity for a paradigm shift in defense architecture becomes existential. This research articulates a transition from centralized, platform-centric escorts to decentralized, network-resilient frameworks defined by Multi-Agent Deep Reinforcement Learning (MADRL) and hardware-encrypted tactical kill chains. By synthesizing advanced electronic maneuver warfare, semantic interoperability via the S-100 standard, and bio-inspired crisis management heuristics, these models provide a high-fidelity blueprint for securing strategic maritime passages and critical civil assets against the volatility of intensifying regional conflict.

This review critically examines Patrick R. Nicolas's article, "The Mathematics of World Models," which explores the integration of advanced mathematical frameworks-such as differential geometry, algebraic topology, and graph theory-into abstract world models (AWMs) in artificial intelligence (AI). The article posits that AWMs address limitations in large language models (LLMs) by embedding experiential, geometric, and physical representations into latent spaces, enabling autonomous reasoning. Key postulates include the shift from generative reconstruction to latent prediction, the enforcement of invariance and equivariance constraints, and the categorization of world models into standard, probabilistic, and latent manifold types. Hypotheses center on the efficacy of geometric priors for preventing latent space collapse and enhancing generalization. Results from referenced models, such as Joint Embedding Predictive Architecture (JEPA) and Spatial Intelligence, demonstrate improved efficiency and reasoning capabilities. This review contextualizes Nicolas's work within broader AI research, highlighting similar efforts in neuro-symbolic predicates and reinforcement learning-based world models. Algebraic interpretations frame latent spaces as Markov Decision Processes (MDPs) with learnable encodings, while geometric views conceptualize them as Riemannian manifolds preserving symmetries. Current Mamun, SM (2026)………………………1 Review of "The Mathematics of World Models" by Patrick R. Nicolas: Advancements in Abstract World Models and Their Mathematical Foundations trends indicate rapid progress toward scalable, interpretable AI, with future directions pointing to hybrid human-AI mathematical discovery. The analysis underscores implications for robotics, simulation, and AGI, while noting challenges like computational scalability and ethical integration. Mamun, SM (2026)………………………2

While the conceptual underpinnings of generative AI have been established for more than half a century, the applications that have captured the public's imagination emerged only in the last decade. These applications include the generation of natural language, 3D realistic computer graphics, and convincingly photorealistic images of people and animals, the development of conversational agents, and text-to-image synthesis.

This paper proposes latent-space auditing as practical alignment infrastructure for multi-agent AI systems. The central argument is that alignment failures are fundamentally semantic rather than technical — AI systems act as miscalibrated mirrors, reflecting what humans said rather than what they meant. In multi-agent pipelines, small semantic gaps between agents compound into large-scale execution errors, a phenomenon termed semantic drift. InterOrdra, an open-source framework using BERT embeddings and topological clustering, is presented as a prototype audit layer that detects structural divergence in latent space between agent handoffs. The Mirror Protocol extends this detection into a collaborative alignment process: detect, pause, reframe, negotiate, act — enabling human-AI systems to verify shared understanding before execution proceeds. Together these components form dynamic alignment infrastructure that operates at the point of action, not only at training time.

Generative AI (GenAI) and Agentic AI are considered the most important state-of-the-art approaches that are shaping the future of healthcare solutions. This systematic literature review (SLR) examines the capabilities & applications as well as clinical benefits for both Generative AI (GenAI) and the Agentic AI in healthcare. This review started with the identification of 87 works from Scopus and PubMed databases. After applying eligibility criteria, duplicates removal, and full-text screening, a total of 18 records published between 2024 and 2025 were included for the final study. The review revealed how Generative AI and Agentic AI are being applied across key areas in healthcare. GenAI is mainly used for diagnostics, automated documentation, and simulation-based training, while Agentic AI supports autonomous tasks and interactive multilingual tools. The findings show that successful AI adoption depends not just on technology, but also on thoughtful workflow design, clinician expertise, organizational readiness, and system integration. Thematic opportunities emerged in workflow optimization, clinical decision support, training and knowledge management, and patient engagement. Although research on complex and goal-directed Agentic AI applications is still limited, these AI approaches offer significant promise to enhance healthcare delivery and guide practical, responsible implementation.

A  systems-oriented  approach  to  AI  Ethics  coupled  with  a  risk-based  framework  can inform  the  development  of  Agentic  AI  and  Autonomous  Decision-Making  Systems capable of behaving in an ethical manner without the need for Manual Ethical Control. Artificial  Intelligence  (AI)  an  autonomous  decision-making  systems  can  significantly benefit  humanity,  as  demonstrated  by  their  deployment  in  a  range  of  critical  sectors including healthcare, energy, education, scientific research, and safety-critical activities. However,  the  development  of  Agentic  AI  with  the  capacity  to  independently  achieve goals in a manner similar to that of humans remains a subject of active ongoing research.The  decision-making  processes  of  these  Agentic  AIs  can  differ  from  traditional statistical  decision-making  processes,  which  are  able  to  support  Decision  Automation and Guidance without the need for Manual Ethical Control. In contrast, Ethical Control Mechanisms deal with an entirely different aspect of AI safety. These systems have the capacity of Ethical Operation that remains incomparable to current AI and autonomous decision-making systems.

Artificial intelligence (AI), especially generative AI, has moved from pilot to production across industries, reshaping project delivery from planning to execution. Adoption is rapid, yet outcomes remain uneven: many teams report frustration when outputs sound plausible but lack accuracy or context. This guide addresses that gap by combining evidence-based practices with PMI-aligned governance. It explains how large language models (LLMs) work (probabilistic next-token prediction), why hallucinations occur, and how to mitigate them through structured prompting and risk-based controls. It defines key termscomplicated vs. complex tasks, hallucination, and sets a clear boundary: AI accelerates well-defined, low-risk work; humans retain accountability for complex, high-consequence decisions. Practical tools include the CAPTURES™ prompt pattern, a 90-day rollout plan and lightweight controls mapped to ISO/IEC 42001. The goal is to deliver measurable cycle-time and clarity gains while maintaining trust and ethical standards.

This paper introduces the Human Enhancement Quotient (HEQ) and Augmented Intelligence Score (AIS), a cross-platform, behavior-anchored, governance-integrated framework for measuring cognitive amplification through human-AI collaboration. Grounded in established psychometric methodology adapted for AI-context assessment, HEQ tracks four dimensions — Cognitive Agility Speed (CAS), Ethical Alignment Index (EAI), Collaborative Intelligence Quotient (CIQ), and Adaptive Growth Rate (AGR) — validated across five AI platforms using the HAIA-RECCLIN methodology. Released for scholarly review and collaboration. No commercial use without written permission.

Large Language Models have redefined human-machine interaction, yet most deployments remain interface-driven and stateless. This article presents Intelligent LLM as a structured AI ecosystem designed to integrate persistent context, priority-based knowledge grounding, multiagent coordination, and governance-aware intelligence into a unified architecture. Rather than treating LLMs as isolated response engines, Intelligent LLM reframes them as components within a coordinated cognitive system. This article consolidates the Intelligent LLM framework and establishes the foundation for an upcoming formal research paper detailing system architecture, algorithms, and evaluation methodology.

This paper presents the cooperative leaner modeling process based on is actions and interactions within a teaching-learning session accomplished through the synchronous and asynchronous communication resources in a distance learning environment. The overall modelling happens within a computer supported collaborative intelligent learning environment (MathNet) designed for combining contributions from the intelligent tutoring system domain, web based-learning area, multiagent systems and cooperative learning paradigms. The cooperative leaner agent stores actions and interactions accomplished by a group during the learning sessions providing information to the cooperative supervisor agent and cooperative learning model for monitoring and reinforcing group cooperations.

Internet of Things (IoT) is the interconnection of physical objects or devices that can transmit and receive data through the internet without human involvement. With the advancement in IoT devices particularly in healthcare sector, huge amount of data is collected from different sensors and all this data are transferred and stored in cloud. It becomes difficult to handle such huge amount of data in cloud specially the healthcare data where it requires real time data computation and storage. Security of the data is also major challenge in cloud. Fog computing is the answer to overcome the challenges. Fog nodes works at the edge side and enhances data security, accuracy, consistency and reduces the latency rate which is an important factor for application like medical data. Implementation work is also described in the paper where a digital human temperature sensor device is built using DS18B20 temperature sensor. The data collected from it is being encrypted in fog node using Advance Encryption Standard(AES) algorithm and it is send to cloud. Therefore, the security of the health care data is enhanced using Fog computing.

We present OpenCLAW-P2P, a decentralized peer-to-peer framework that enables autonomous AI agents to form a global network for collective intelligence. Built as an extension to the OpenCLAW personal AI assistant platform, the system allows agents to discover peers through a Kademlia-based Distributed Hash Table (DHT), propagate knowledge via gossip protocols derived from epidemic dissemination theory, distribute computational tasks with reputation-weighted allocation, conduct federated learning with differential privacy guarantees, and achieve consensus on results and self-improvement proposals through a Byzantine Fault Tolerant (BFT) voting mechanism. The architecture comprises four principal subsystems: (i) the Peer Node, managing identity, routing, and gossip; (ii) the Distributed Compute Engine, orchestrating task allocation and federated learning; (iii) the Consensus Protocol, governing result verification and self-improvement governance; and (iv) the Network Transport, implementing WebSocket-based communication with WebRTC support for browser agents. Preliminary simulation results on a 20-node network demonstrate knowledge propagation convergence within three gossip rounds (under ten seconds at typical latencies), reputation system stabilization after approximately fifty task cycles, and consensus finalization within sixty seconds for 95% of proposals under conditions with up to 20% Byzantine peers. The architecture is designed to unify computational power and AI capabilities at a global scale, with particular focus on scientific research, medical applications, and the advancement of agent self-improvement as a pathway toward Artificial General Intelligence (AGI) and Artificial Superintelligence (ASI). We describe the complete system architecture, protocol specifications, safety mechanisms, and present preliminary results from network simulations. The implementation is fully open-source, written in TypeScript targeting Node.js 22+, and integrated with the OpenCLAW ecosystem through its skill-based extensibility model.

Recently, interactive storytelling systems - systems that allow a user to make decisions that can potentially impact the direction of a narrative - have been applied to training and education. Interactive storytelling systems often rely on an automated story director to manage the user's experience. The focus of an automated director is the emergence of a narrative-like experience for the

Endowing robot swarm systems with biological morphogenetic behavior makes swarm shape formation emergent, adaptive, and robust. Morphogenesis allows millions of cells to self-organize into intricate structures with a wide variety of functional shapes during embryonic development. This process emerges from local interactions of cells under the control of gene circuits that are identical in every cell, robust to intrinsic noise, and adaptable to changing environments. Constructing human technology with these properties presents an important opportunity in swarm robotic applications ranging from construction to exploration. Morphogenesis in nature may use two different approaches: hierarchical, top-down control or spontaneously self-organizing dynamics such as reaction-diffusion Turing patterns. Here, we provide a demonstration of purely self-organizing behaviors to create emergent morphologies in large swarms of real robots. The robots achieve this collective organization without any ...

AI has proven highly successful at urban planning analysis-learning patterns from data to predict future conditions. The next frontier is AI-assisted decision-making: agents that recommend sites, allocate resources, and evaluate trade-offs while reasoning transparently about constraints and stakeholder values. Recent breakthroughs in reasoning AI-CoT 1 prompting, ReAct 2 , and multi-agent collaboration frameworks-now make this vision achievable. This position paper presents the Agentic Urban Planning AI Framework for reasoning-capable planning agents that integrates three cognitive layers (Perception, Foundation, Reasoning) with six logic components (Analysis, Generation, Verification, Evaluation, Collaboration, Decision) through a multi-agents collaboration framework. We demonstrate why planning decisions require explicit reasoning capabilities that are value-based (applying normative principles), rulegrounded (guaranteeing constraint satisfaction), and explainable (generating transparent justifications)-requirements that statistical learning alone cannot fulfill. We compare reasoning agents with statistical learning, present a comprehensive architecture with benchmark evaluation metrics, and outline critical research challenges. This framework shows how AI agents can augment human planners by systematically exploring solution spaces, verifying regulatory compliance, and deliberating over trade-offs transparently-not replacing human judgment but amplifying it with computational reasoning capabilities.

The evolution of Large Language Models, Roboadvisors or chatbots, and Machine Learning Models have opened the possibilities for automation across sectors to innovate and introduce new capabilities as part of their business process. This review paper targets all the critical aspects of artificial intelligence and machine learning models and their impact on the successful deployment of the Agentic AI capability. Agents can not only participate but also assist, self-organize, and negotiate using parallel workstreams and cognitive architectures. They solve problems iteratively based on their cognitive level and domain, creating value in the financial services market, where opportunities are vast. This study further includes principles for Agentic AI, its lifecycle, various key components, impact on the financial industry, architectural considerations, data considerations, benchmarking, opportunities, future capabilities, challenges, and more.

Organizations deploying multi-AI workflows face a structural governance gap: orchestration frameworks route tasks between AI platforms, but no published framework provides the accountability, audit trail architecture, provider plurality, automation bias detection, and regulatory compliance mapping required for audit-grade operations. This paper presents HAIA-RECCLIN (Human AI Assistant with Researcher, Editor, Coder, Calculator, Liaison, Ideator, and Navigator roles), a governance architecture specification for a non-cognitive autonomous agent that mediates between human operators and multiple independent AI platforms. The architecture enforces mandatory human checkpoints, append-only tamper-evident audit trails, cryptographic hash chaining, provider rotation protocols, and threshold-based automation bias detection. It performs zero cognitive work: it dispatches, collects, routes, logs, and pauses. Three operating models scale governance intensity from factory-speed single-endpoint approval through full human arbitration at every checkpoint to manual oversight with automated logging. The framework was developed through iterative refinement across ten independent AI platforms and validated through multiple operational proof-of-concept deployments, including a 96-checkpoint manuscript production documented in Governing AI: When Capability Exceeds Control (Puglisi, 2025), a five-platform thought leader evaluation convergence study, and adversarial stress testing across nine platforms. The non-cognitive agent specified in this architecture is under preliminary development as HAIA-GOPEL (Governance Orchestrator Policy Enforcement Layer). To the best of the author's knowledge, no published framework integrates non-cognitive agent design, mandatory provider plurality, cryptographic audit trails, automation bias detection, and crossframework regulatory compliance mapping into a single governance architecture. This integration gap, verified through independent search across four AI platforms, constitutes the primary contribution of this work.

Agentic AI systems are a recently emerged and important approach that goes beyond traditional AI, generative AI, and autonomous systems by focusing on autonomy, adaptability, and goal-driven reasoning. This study provides a clear review of agentic AI systems by bringing together their definitions, frameworks, and architectures, and by comparing them with related areas like generative AI, autonomic computing, and multi-agent systems. To do this, we reviewed 143 primary studies on current LLM-based and non-LLM-driven agentic systems and examined how they support planning, memory, reflection, and goal pursuit. Furthermore, we classified architectural models, input-output mechanisms, and applications based on their task domains where agentic AI is applied, supported using tabular summaries that highlight real-world case studies. Evaluation metrics were classified as qualitative and quantitative measures, along with available testing methods of agentic AI systems to check the system's performance and reliability. This study also highlights the main challenges and limitations of agentic AI, covering technical, architectural, coordination, ethical, and security issues. We organized the conceptual foundations, available tools, architectures, and evaluation metrics in this research, which defines a structured foundation for understanding and advancing agentic AI. These findings aim to help researchers and developers build better, clearer, and more adaptable systems that support responsible deployment in different domains.

Software bots operating in multiple virtual digital platforms must understand the platforms' affordances and behave like human users. Platform affordances or features differ from one application platform to another or through a life cycle, requiring such bots to be adaptable. Moreover, bots in such platforms could cooperate with humans or other software agents for work or to learn specific behavior patterns. However, present-day bots, particularly chatbots, other than language processing and prediction, are far from reaching a human user's behavior level within complex business information systems. They lack the cognitive capabilities to sense and act in such virtual environments, rendering their development a challenge to artificial general intelligence research. In this study, we problematize and investigate assumptions in conceptualizing software bot architecture by directing attention to significant architectural research challenges in developing cognitive bots endowed with complex behavior for operation on information systems. As an outlook, we propose alternate architectural assumptions to consider in future bot design and bot development frameworks.

Enterprise-scale orchestration of Generative AI (Gen AI) provides an intelligent integration of ecosystem actors within processes and workflows. The deployment of small enterprise-specific language models (LMs) combined with autonomous Behavioural LLM Agent architectures supports sophisticated orchestration of IT Service Management (ITSM) and Human Resource Service Delivery (HRSD) automation Use Cases, Including Digital Incident Management, Employee Onboarding, Offboarding, and Activity Allocation. Unlike traditional orchestration techniques, which typically rely on hardcoded workflows and limited logic, Gen AI techniques allow for enhanced coordination among Process-Performer Agents with minimal human involvement during execution. An orchestrating agent guides execution, managing Process Performer communication, triggering actions based on input messages, keeping track of execution state, and rolling back processes when required. Agents can collaborate or operate independently based on trigger conditions and a behaviour set associated with each Use Case. During execution, Gen AI supports the automated generation of documentation, audit logging, and telemetry collection for the orchestration infrastructure. In this way, the Gen AI approach enables the intelligent, autonomous orchestration of processes and workflows across enterprise ecosystems-a goal that is exceedingly complex to achieve using traditional techniques.

The field of artificial intelligence is currently navigating a profound transition from the paradigm of static, predictive language modeling to that of dynamic, agentic reasoning. This shift reframes Large Language Models (LLMs) not merely as engines for stochastic sequence generation but as the cognitive cores of autonomous agents capable of perception, planning, action execution, and continual self-improvement. This survey provides an exhaustive and rigorous analysis of the agentic reasoning landscape, organizing the domain into a three-layered taxonomy: Foundational Agentic Reasoning, which establishes core single-agent capabilities such as planning, tool use, and search; Self-Evolving Agentic Reasoning, which examines mechanisms for feedback integration, memory persistence, and lifelong adaptation; and Collective Multi-Agent Reasoning, which explores the emergent intelligence of coordinated agent systems. Across these layers, we critically evaluate the dichotomy between in-context reasoning (inference-time orchestration without parameter updates) and post-training reasoning (reinforcement learning, supervised fine-tuning, and policy optimization). We further synthesize insights from recent benchmarks (e.g., GAIA, SWE-bench, ALFWorld) and real-world applications to assess the current state of the art. Crucially, this report addresses significant gaps in prior literature by providing a deep architectural analysis of generative world models, latent dynamics learning, and the rigorous governance frameworks required to mitigate the unique risks of autonomous agency, such as Mamun, SM (2026)………………………………………..1 Agentic Reasoning for Large Language Models: A Comprehensive Survey and Critical Analysis hallucination cascades and cognitive degradation.