Vivek Acharya

Vivek Acharya

Multi-agent large language model (LLM) systems are rapidly emerging as the dominant architecture for enterprise AI automation, yet production deployments exhibit failure rates between 41% and 86.7%, with nearly 79% of failures originating from specification and coordination issues rather than model capability limitations. This paper identifies Semantic Intent Divergence--the phenomenon whereby cooperating LLM agents develop inconsistent interpretations of shared objectives due to siloed context and absent process models--as a primary yet formally unaddressed root cause of multi-agent failure in enterprise settings. We propose the Semantic Consensus Framework (SCF), a process-aware middleware comprising six components: a Process Context Layer for shared operational semantics, a Semantic Intent Graph for formal intent representation, a Conflict Detection Engine for real-time identification of contradictory, contention-based, and causally invalid intent combinations, a Consensus Resolution Protocol using a policy--authority--temporal hierarchy, a Drift Monitor for detecting gradual semantic divergence, and a Process-Aware Governance Integration layer for organizational policy enforcement. Evaluation across 600 runs spanning three multi-agent frameworks (AutoGen, CrewAI, LangGraph) and four enterprise scenarios demonstrates that SCF is the only approach to achieve 100% workflow completion--compared to 25.1% for the next-best baseline--while detecting 65.2% of semantic conflicts with 27.9% precision and providing complete governance audit trails. The framework is protocol-agnostic and compatible with MCP and A2A communication standards.

Vivek Acharya

The rapid adoption of agentic AI in enterprise business operations--autonomous systems capable of planning, reasoning, and executing multi-step workflows--has created an urgent governance crisis. Organizations face uncontrolled agent sprawl: the proliferation of redundant, ungoverned, and conflicting AI agents across business functions. Industry surveys report that only 21% of enterprises have mature governance models for autonomous agents, while 40% of agentic AI projects are projected to fail by 2027 due to inadequate governance and risk controls. Despite growing acknowledgment of this challenge, academic literature lacks a formal, empirically validated governance maturity model connecting governance capability to measurable business outcomes. This paper introduces the Agentic AI Governance Maturity Model (AAGMM), a five-level framework spanning 12 governance domains, grounded in NIST AI RMF and ISO/IEC 42001 standards. We additionally propose a novel taxonomy of agent sprawl patterns--functional duplication, shadow agents, orphaned agents, permission creep, and unmonitored delegation chains--each linked to quantifiable business cost models. The framework is validated through 750 simulation runs across five enterprise scenarios and five governance maturity levels, measuring business outcomes including cost containment, risk incident rates, operational efficiency, and decision quality. Results demonstrate statistically significant differences (p < 0.001, large effect sizes d > 2.0) between all governance maturity levels, with Level 4-5 organizations achieving 94.3% lower sprawl indices, 96.4% fewer risk incidents, and 32.6% higher effective task completion rates compared to Level 1. The AAGMM provides practitioners with an actionable roadmap for governing autonomous AI agents while maximizing business returns.

Vivek Acharya

Artificial intelligence (AI) agents are increasingly capable of initiating financial transactions on behalf of users or other agents. This evolution introduces a fundamental challenge: verifying both the authenticity of an autonomous agent and the true intent behind its transactions in a decentralized, trustless environment. Traditional payment systems assume human authorization, but autonomous, agent-led payments remove that safeguard. This paper presents a blockchain-based framework that cryptographically authenticates and verifies the intent of every AI-initiated transaction. The proposed system leverages decentralized identity (DID) standards and verifiable credentials to establish agent identities, on-chain intent proofs to record user authorization, and zero-knowledge proofs (ZKPs) to preserve privacy while ensuring policy compliance. Additionally, secure execution environments (TEE-based attestations) guarantee the integrity of agent reasoning and execution. The hybrid on-chain/off-chain architecture provides an immutable audit trail linking user intent to payment outcome. Through qualitative analysis, the framework demonstrates strong resistance to impersonation, unauthorized transactions, and misalignment of intent. This work lays the foundation for secure, auditable, and intent-aware autonomous economic agents, enabling a future of verifiable trust and accountability in AI-driven financial ecosystems.

Vivek Acharya

The United States spends nearly 17% of GDP on healthcare yet continues to face uneven access and outcomes. This well-known trade-off among cost, quality, and access - the "iron triangle" - motivates a system-level redesign. This paper proposes an Intelligent Healthcare Ecosystem (iHE): an integrated, data-driven framework that uses generative AI and large language models, federated learning, interoperability standards (FHIR, TEFCA), and digital twins to improve access and quality while lowering cost. We review historical spending trends, waste, and international comparisons; introduce a value equation that jointly optimizes access, quality, and cost; and synthesize evidence on the enabling technologies and operating model for iHE. Methods follow a narrative review of recent literature and policy reports. Results outline core components (AI decision support, interoperability, telehealth, automation) and show how iHE can reduce waste, personalize care, and support value-based payment while addressing privacy, bias, and adoption challenges. We argue that a coordinated iHE can bend - if not break - the iron triangle, moving the system toward care that is more accessible, affordable, and high quality.

Vivek Acharya

Generative AI is reshaping how software is designed, written, and maintained. Advances in large language models (LLMs) are enabling new development styles - from chat-oriented programming and 'vibe coding' to agentic programming - that can accelerate productivity and broaden access. This paper examines how AI-assisted techniques are changing software engineering practice, and the related issues of trust, accountability, and shifting skills. We survey iterative chat-based development, multi-agent systems, dynamic prompt orchestration, and integration via the Model Context Protocol (MCP). Using case studies and industry data, we outline both the opportunities (faster cycles, democratized coding) and the challenges (model reliability and cost) of applying generative AI to coding. We describe new roles, skills, and best practices for using AI in a responsible and effective way.