Harsh Rathour
Harsh Rathour is a performance marketer and content writer at Kore.ai, blending data analytics, AI-driven strategy, and storytelling to drive marketing outcomes.
Articles by this author
AI agent sprawl: What it is, why it happens, and how to stop it
AI agent sprawl occurs when enterprises deploy increasing numbers of AI agents without centralized visibility, governance, ownership, or lifecycle management. This article examines risks including security, compliance, permissions, data access, and operational complexity. It explores agent inventories, identity management, observability, governance, and lifecycle controls. Kore.ai Artemis addresses these challenges through centralized agent management, ABL, governance, observability, and auditability for enterprise-scale agentic AI.
Enterprise AI observability gap: Why your platform is only logging 15% of every agent decision
The enterprise AI observability gap occurs when platforms capture AI inputs and outputs but cannot explain the decisions made between them. This article explains why AI agent observability, decision tracing, causal lineage, and auditability are essential for production AI. It examines how agents make multiple decisions involving intent, context, tools, guardrails, policies, and handoffs, while many systems capture only a fraction of these events. Kore.ai advocates full-trace observability with structured execution events, real-time monitoring, and governance-ready audit trails to make AI decisions explainable, debuggable, and accountable.
Why enterprise AI projects stall after the pilot: The build trap explained
The Enterprise AI Build Spiral describes how AI programs stall after successful pilots when organizations scale without a shared operational foundation. This article explains how AI governance, observability, compliance, AI agent sprawl, and duplicated infrastructure create unplanned costs and slow enterprise AI adoption. It contrasts AI pilots with production requirements and outlines how shared governance, complete observability, reusable infrastructure, and centralized policy controls help organizations scale enterprise AI more efficiently. The article highlights Kore.ai’s approach to building a shared foundation for production-ready AI agents.
Agentic AI in healthcare: Automating patient management and care coordination
Agentic AI in healthcare helps automate patient management and care coordination by executing multi-step workflows across EHRs, payer systems, pharmacies, and communication channels. This article explores AI agents for patient follow-up, medication adherence, care gap closure, referrals, post-discharge coordination, and proactive patient engagement. It highlights how Kore.ai enables healthcare organizations to orchestrate workflows, apply governance and HIPAA controls, and escalate cases requiring human judgment, helping reduce administrative burden while improving continuity of care and patient outcomes.
Agentic AI in ITSM: Benefits, use-cases, and challenges
Agentic AI in ITSM enables AI agents to autonomously detect, diagnose, resolve, and close IT service management issues across enterprise systems. This article explores AI-powered ITSM, autonomous incident resolution, AI self-service, problem management, CMDB correction, change management, endpoint management, and automated knowledge generation. It explains how reasoning and governed execution work together, while Kore.ai AI for IT connects ITSM, identity, monitoring, and endpoint systems to automate end-to-end workflows with observability, policy controls, and human escalation.
How agentic AI in banking helps enhance customer experience
Agentic AI in banking transforms customer experience by moving beyond reactive chatbots to proactive, personalized, and autonomous service. This article explores how AI agents use customer context to resolve issues, personalize interactions, provide proactive support, enable multilingual service, and automate complex workflows such as fraud resolution, complaints, payments, and loan processes. Kore.ai AI for Banking combines agentic reasoning with deterministic controls, governance, observability, and integrations across banking systems. Real-world deployments demonstrate outcomes including 600K+ annual AI conversations, 2.6M+ automated customer sessions, and 150K+ daily multilingual conversations.