New York, NY – March 14, 2026 – Gloat, a prominent player in skills intelligence and talent marketplace solutions, has officially entered the burgeoning field of AI Agents for Human Resources. The company’s strategic move, announced this week, underscores the escalating competition among core HR technology providers to leverage artificial intelligence for transforming workforce management. Gloat’s new offering, dubbed Gloat Agentic HR, promises to empower organizations to rapidly develop and deploy AI agents that integrate seamlessly with existing enterprise systems and popular communication platforms.
A New Architecture for HR AI: Understanding the Layers of Agentic Intelligence
The introduction of Gloat’s platform necessitates a deeper understanding of the emerging architecture for AI agents within the enterprise. This framework can be broadly categorized into five distinct layers, each building upon the one below.
At the foundational level reside the Systems of Record. These are the robust Human Capital Management (HCM) and Enterprise Resource Planning (ERP) systems, such as Oracle, Workday, SAP, and UKG. They serve as the central repositories for all critical organizational data, including financials, customer information, employee details, inventory, and product catalogs. These systems meticulously store, update, and maintain the core operational data of a company.
Layered above these are Cross-System Applications. In today’s complex corporate IT environments, no single vendor provides a complete solution. Consequently, businesses develop intricate ecosystems of portals, mobile applications, and custom workflows that bridge these core systems with hundreds of specialized applications. These can range from time-tracking systems and Learning Management Systems (LMS) to Applicant Tracking Systems (ATS) and IT provisioning tools. Research indicates that the average large enterprise utilizes over 400 such applications, with more than 100 directly impacting employee experience. Over the past two decades, as businesses transitioned from on-premise to cloud-based solutions, the development of these interconnected ecosystems has accelerated. Companies like ServiceNow, a market leader with significant annual growth, have established dominance in this layer, facilitating global enterprise operations. While platforms such as Microsoft Viva, Teams, and Slack, along with employee experience tools from providers like Workvivo, Firstup, and Staffbase, are widely adopted, many were not initially designed with "agentic" capabilities in mind.
The third layer represents the New Breed of Agents. These AI-powered entities move beyond simple portals or predefined workflows. Agents possess a degree of intelligence about individual users and can proactively perform tasks on their behalf. In the HR domain, this has led to a proliferation of agent tools, developed by both established vendors and agile startups. These tools are designed to facilitate the creation of cross-functional HR solutions that address diverse employee needs.
Crowning this architecture are "Superagents." These advanced tools are capable of orchestrating and accessing multiple functional agents, thereby delivering an even more intuitive and "walk-up-and-use" experience for end-users. This layered approach provides a conceptual model for understanding how AI agents are being integrated into the enterprise fabric.
The current market is experiencing a surge of tools designed for this agentic layer. Prominent examples include Sana (an integration for Workday), Oracle Agent Studio, Microsoft Copilot Studio, Leena.ai, and new agent capabilities emerging from ServiceNow’s acquisition of MoveWorks. SAP is also introducing its Joule Studio, alongside foundational AI agent technologies from major players like Anthropic, OpenAI, and Google. Tools like Galileo, described as an AI advisor for HR professionals, exemplify how these agents can provide employees with direct access to information and capabilities through natural language queries.
The Operationalization of AI Agents: Navigating Complexity and Business Rules
While the potential of AI agents in HR is widely acknowledged, their practical implementation is fraught with challenges related to existing business rules, security protocols, and legacy system constraints.
Consider a hypothetical payroll reconciliation agent. While a vendor like Workday, SAP, or Oracle might offer such a tool, its effectiveness hinges on its ability to be "trained" with a company’s specific business rules and security configurations. This agent would need to monitor payroll transactions, reconciling data related to taxes, timecards, employee movements, terminations, new hires, and pay adjustments. Crucially, its functionality often extends to interactions with other agents, such as those managing compensation or performance, to provide a holistic view.
The importance of robust "agent architecture" becomes evident when contemplating more complex scenarios. Imagine an initiative to redeploy 5,000 employees from declining business units to emerging roles. An AI agent tasked with identifying suitable candidates would need to analyze job fit, assess skill gaps, and predict redeployment potential. To achieve this, the agent must seamlessly interact with a multitude of other agents and systems, including those managing skills inventories, learning and development resources, organizational structures, and even external labor market data.
A sophisticated agent, akin to the "Superagent" concept, would not only identify potential matches but also provide personalized coaching to employees and managers regarding their options. This includes understanding available training programs, addressing geographical constraints, navigating licensing requirements, accounting for union agreements, and considering pay band limitations. The complexity of such an undertaking highlights the necessity for a well-defined and interconnected agent ecosystem.
Beyond workforce redeployment, common use cases for agentic AI in HR include global onboarding processes, managing internal promotions, streamlining annual performance reviews and compensation cycles, optimizing talent acquisition, ensuring employee certifications are up-to-date, and managing complex time and schedule requirements.
The Battle for Enterprise Agents: The Crucial Role of Business Rules and Context
The HR technology landscape is witnessing a fierce competition as vendors race to embed AI agents into their offerings. While some agents provide basic coaching functionalities, others aim to deliver more transformative capabilities. Major enterprise software providers, including Workday, Oracle, SAP, and ServiceNow, are investing heavily in AI studios, enabling organizations to build and customize agents with greater ease.
However, the primary battleground is not solely about acquiring individual agents but about effectively integrating them into a cohesive and functional whole. This integration hinges on managing an organization’s unique business rules, security frameworks, existing workflows, and proprietary "business objects." These objects, such as a company’s specific career framework or performance review model, represent the core logic and intellectual property that define its operational model. As businesses evolve, these rules and objects must adapt, and it is imperative that AI agents can access and operate within this dynamic framework without requiring hard-coded dependencies.
This is where the concept of a "semantic layer" becomes critical. HCM vendors, recognizing this need, are increasingly incorporating their own semantic layers into their agent development tools. Workday, for instance, leverages its Business Process Framework, while other vendors are developing comparable capabilities. The ability of any HR agent or agent development platform to tap into this rich semantic layer is paramount for its success. This ongoing development suggests that the perceived "SaaSpocalypse," a notion of widespread software consolidation, might be tempered by the rise of specialized, interconnected agentic solutions.
Gloat’s Strategic Entry: Loomra and the Power of Workforce Context
Gloat’s latest initiative aims to circumvent the limitations of traditional approaches by offering a novel solution to this integration challenge. The company has developed an "agent-driven auto-discovery ‘injector’" named Loomra. This sophisticated technology mines and replicates entities, workflows, and business rules directly from an organization’s HCM system, crucially maintaining synchronization as the underlying system evolves. Loomra acts as a semantic layer, effectively capturing the essence of an organization’s operational logic, thereby enabling the creation of applications that are deeply integrated and responsive to existing business processes.
Following the deployment of Loomra, Gloat provides an intuitive agent builder interface. This visual tool allows HR departments to construct their own custom AI agents, designed to operate directly within familiar environments such as Microsoft Teams, Copilot, and Slack. Gloat has already pre-built a suite of agents for critical HR functions, including Workforce Redeployment, Career Development, Internal Talent Sourcing, Succession Planning, and Learning & Reskilling. These initial offerings leverage Gloat’s existing expertise in these areas.
Workforce Context as the Differentiating Factor
What truly distinguishes Gloat’s Agentic HR platform is the underlying workforce context provided by its Workforce Context Engine, Loomra. This engine is built upon nearly a decade of accumulating and modeling enterprise-scale workforce data. It encompasses detailed analyses of skill adjacencies, typical career trajectories, organizational patterns, and intricate workforce relationships derived from millions of employees globally.
This deep contextual understanding empowers Gloat’s agents to transcend mere information retrieval. Instead of simply responding to queries like "show me employees who know Python," these agents can perform sophisticated reasoning. They can identify employees with the potential to transition into AI engineering roles, pinpoint teams most vulnerable to future skill gaps, and recommend optimal talent redeployment strategies in response to shifting business priorities. This proactive and insightful capability represents a significant advancement over basic data lookup functions.
Competitive Landscape and Strategic Implications
Gloat’s offering presents a compelling proposition: organizations can potentially "start with Gloat" to build advanced AI capabilities without waiting for their incumbent HCM vendors to develop comparable solutions. The platform’s open architecture facilitates integration with other internal systems, and its unique context engine sets it apart. While other HCM vendors are developing their own agent studios—such as Sana for Workday, Oracle AI Studio, and SAP’s Joule Studio—Gloat claims a distinct advantage in its depth of workforce context.
However, the market for enterprise AI agents is intensely competitive. Gloat faces the challenge of convincing potential clients that its tools offer superior ease of use, seamless integration with their existing HCM infrastructure, and a more profound level of context-aware intelligence compared to solutions offered by their primary HCM providers. As the enterprise IT paradigm shifts from "systems of record" to "systems of context," Gloat has positioned itself to potentially lead this transformation.
The Future of Enterprise AI Development
The evolution of application development tools has historically favored platforms that align with an organization’s existing infrastructure. New tools, from early web development platforms to modern AI coding assistants, often see adoption based on compatibility and perceived benefits within established tech stacks.
Gloat’s Agentic HR platform represents an innovative leap forward in the agent industry, pushing the boundaries of what is currently possible. The success of this venture will likely depend on its ability to demonstrate tangible value and seamless integration within diverse enterprise environments. As early customer adoption stories begin to emerge, the industry will gain further clarity on how Gloat’s approach will shape the future of AI-driven HR transformation.
Additional Information:
- Workday and Sana Unveil A Bold New Strategy For AI: [Link to relevant article, e.g., https://joshbersin.com/2026/03/workday-and-sana-unveil-a-bold-new-strategy-for-ai/]
- Agents, Superagents, and Intelligent Orchestration: 2026 Imperatives for Enterprise AI: [Link to relevant article, e.g., https://joshbersin.com/imperatives]
- The L&D Revolution Has Arrived: AI Enables Dynamic Enablement For All: [Link to relevant article, e.g., https://joshbersin.com/learning2026]
- Get Galileo, the AI Superagent for HR: [Link to relevant product page, e.g., https://getgalileo.ai/]
