This week, Gloat, a recognized leader in skills intelligence and talent marketplace solutions, has made a significant and strategic entry into the burgeoning field of AI Agents for Human Resources. This move not only underscores Gloat’s commitment to innovation but also highlights the intensifying competition among core HR technology providers to harness the power of artificial intelligence. The company’s new offering, Gloat Agentic HR, aims to empower organizations to rapidly develop and deploy AI agents that integrate seamlessly with existing enterprise systems and widely used communication platforms.
The core of Gloat’s new proposition lies in its "Agentic HR" toolset. This platform is designed to leverage the established business rules, security protocols, and data integrity of leading HR systems such as Oracle, Workday, and SuccessFactors. By abstracting these complex backend functionalities, Gloat enables businesses to quickly construct AI agents capable of operating within popular environments like Microsoft Copilot, Microsoft Teams, Slack, and other emerging AI applications. This approach seeks to bridge the gap between the sophisticated data housed within HRIS platforms and the user-friendly, conversational interfaces that employees increasingly expect.
The Evolving Architecture of Agentic AI in HR
To fully grasp the significance of Gloat’s announcement, it is essential to understand the layered architecture that defines the current landscape of AI agents. This framework provides a useful lens through which to analyze the capabilities and challenges of integrating AI into enterprise operations, particularly within HR.
At the foundational level reside the "systems of record." These are the robust, often monolithic, enterprise resource planning (ERP) and human capital management (HCM) applications that serve as the single source of truth for an organization’s critical data. This category includes giants like Workday, SAP, Oracle, and UKG, which manage vast databases encompassing financial information, customer details, employee records, inventory, and product data. These systems are the bedrock upon which all other business processes are built.
Sitting atop these systems of record is a layer of "cross-system applications." Recognizing that no single vendor can fulfill every organizational need, companies have historically developed and adopted a multitude of specialized applications and integrated platforms. These can include employee portals, mobile applications, and complex workflows that span multiple systems. This ecosystem often incorporates hundreds of specialized tools, such as learning management systems (LMS), applicant tracking systems (ATS), time-tracking solutions, and IT provisioning software. In large enterprises, it is not uncommon to find over 400 such applications, with more than 100 directly impacting the daily experience of employees. Over the past two decades, as organizations have migrated from on-premises infrastructure to cloud-based solutions, this ecosystem has grown exponentially. Companies like ServiceNow, a global leader in digital workflow automation with a market capitalization exceeding $13.5 billion and consistent annual growth around 20%, have become dominant players in managing this complex layer for global organizations. Employee experience platforms, including Microsoft Viva, Zoom’s Workvivo, Firstup, and Staffbase, as well as tools built on Google and Slack, are also prevalent. However, many of these platforms were not initially designed with "agentic" capabilities in mind.
The third, and arguably most transformative, layer is comprised of "Agents." Unlike traditional portals or workflow tools, agents possess a degree of intelligence about the user and can perform actions on their behalf. In the HR domain, this has led to an explosion of agent tools, ranging from innovative startups to embedded functionalities within enterprise systems. These agents aim to facilitate the creation of cross-functional solutions tailored to employee needs.
Further up the stack are "Superagents." These advanced tools orchestrate and integrate multiple functional agents, providing users with an even more intuitive and "walk-up-and-use" experience. The goal is to abstract away the complexity of individual agents and present a unified, intelligent interface.
The current market is witnessing a significant influx of tools catering to these agent layers. This includes offerings from established HR tech providers like Workday (Sana), Oracle (Oracle Agent Studio), and SAP (Joule Studio), as well as Microsoft’s Copilot Studio. Startups such as Leena.ai and companies leveraging foundational AI models from Anthropic, OpenAI, and Google are also actively contributing to this dynamic ecosystem. For instance, Galileo, described as an AI-powered advisor or HR professional agent, exemplifies this trend by providing employees with a conversational interface to access information and utilize underlying agents.
The Operationalization of AI Agents: Navigating Complexity
While the advent of AI agents presents exciting possibilities, their practical implementation within HR departments is far from straightforward. The successful deployment of these agents hinges on their ability to navigate the intricate web of business rules, security protocols, and legacy systems that characterize most large organizations.
Consider, for example, a payroll reconciliation agent offered by a major HCM vendor. Such an agent might be designed to monitor payroll transactions, reconciling tax data, time cards, employee status changes (new hires, terminations), and pay adjustments. However, its efficacy is entirely dependent on its ability to be "trained" with the specific rules and security parameters of the client company. Furthermore, if the agent is tasked with monitoring pay equity or incorporating performance-based adjustments, it must be capable of interoperating with other relevant agents and systems. This highlights the critical importance of robust "agent architecture."
The complexity becomes even more apparent when envisioning sophisticated use cases. Imagine an organization aiming to redeploy 5,000 employees from a declining business unit to new, emerging roles. An agent designed for this purpose would need to assess job fit, identify skill gaps, and evaluate the potential for successful transitions. To achieve this, the agent would require access to and understanding of data from multiple sources, including skills databases, job descriptions, training records, and potentially performance management systems. As demonstrated by the development of advanced agents within platforms like Galileo, such a "Superagent" would need to comprehend intricate skill and job requirements, offer personalized coaching to employees and managers, identify relevant training opportunities, and factor in critical constraints like geographical location, necessary certifications, union agreements, and compensation structures. The sheer number of variables involved underscores the intricate nature of building truly effective, context-aware AI agents.
Beyond workforce redeployment, common applications for agentic AI in HR span a wide spectrum of critical functions. These include streamlining global onboarding processes, facilitating internal promotions, managing annual reviews and compensation cycles, optimizing the entire talent acquisition funnel, ensuring employee certifications are current, and managing complex time and schedule requirements. Each of these areas presents unique data integration and rule-based challenges that AI agents must address.
The Battle for Enterprise Agents: The Centrality of Business Rules
The race to build and deploy AI agents for HR is characterized by intense competition, with nearly every major HR technology vendor actively developing their own solutions. While some offerings focus on simpler coaching functionalities, others aim to provide more comprehensive capabilities. Leading vendors, particularly Workday, Oracle, SAP, and ServiceNow, are investing heavily in "AI studios" – development environments designed to simplify the creation and deployment of these agents.
However, the primary battleground is not merely about which individual agent is superior, but rather how effectively these agents can be integrated and orchestrated. This integration challenge is deeply intertwined with the management of an organization’s unique business rules, existing security frameworks, established workflows, and proprietary business "objects." These objects can range from a company’s defined career framework to its performance review methodology.
These rules and rubrics represent the core intellectual property and operational logic of a business. As organizations evolve, so too do these underlying principles. The challenge lies in ensuring that AI agents can dynamically access and apply these rules without requiring them to be hard-coded directly into the agent’s logic. This necessitates a robust "semantic layer" – a sophisticated abstraction that accurately represents and maintains an organization’s business context.
The established HCM vendors, such as Workday, Oracle, SAP, and UKG, already possess sophisticated semantic layers through their Business Process Frameworks and similar constructs. They are actively integrating these foundational elements into their agent development tools. Consequently, any emerging HR agent or agent development platform will inevitably seek to tap into this vital layer of organizational context. This dynamic may even challenge predictions of a "SaaSpocalypse," suggesting a continued reliance on deeply integrated enterprise systems.
Gloat’s Strategic Pivot: Loomra and Agentic HR
Gloat’s latest initiative, Gloat Agentic HR, aims to liberate organizations from the limitations of traditional, siloed approaches. The company has developed an "agent-driven auto-discovery ‘injector’" named Loomra. This innovative technology is designed to meticulously mine an organization’s enterprise systems, identifying and cataloging critical entities, workflows, and business rules. Crucially, Loomra continuously monitors these systems, ensuring that the extracted business logic remains synchronized with any changes made within the core HCM platforms. In essence, Loomra creates a dynamic replica of an organization’s rules and objects, providing a stable and accessible foundation upon which custom applications and agents can be built.
Following the establishment of this semantic layer, Gloat offers an intuitive agent builder. This visual development tool allows HR professionals and IT teams to construct their own custom agents. These agents can then be seamlessly integrated into popular collaboration and productivity platforms such as Microsoft Teams, Microsoft Copilot, and Slack, among others.
Gloat has already developed a suite of pre-built agents addressing key HR functions where it possesses significant expertise. These include agents for Workforce Redeployment, Career Development, Internal Talent Sourcing, Succession Planning, and Learning & Reskilling. These offerings leverage Gloat’s established capabilities in understanding employee skills and career pathways.
The Power of Workforce Context as a Foundation
What truly distinguishes Gloat’s Agentic HR platform is the robust workforce context layer provided by Loomra. Built upon nearly a decade of collecting and analyzing enterprise-scale workforce data, Loomra’s Workforce Context Engine models intricate skill adjacencies, predictable career trajectories, organizational patterns, and workforce relationships across millions of employees globally.
This deep contextual understanding empowers Gloat’s agents to move beyond simple information retrieval. Instead of merely answering queries like "show me employees who know Python," these agents can engage in sophisticated reasoning. They can identify which employees are best positioned to transition into AI engineering roles within the coming year, pinpoint teams most vulnerable to future skill gaps, and recommend optimal talent redeployments as business priorities shift. This proactive, predictive capability represents a significant leap forward in HR technology.
Competitive Landscape and Gloat’s Positioning
Gloat’s approach offers a compelling proposition: organizations can potentially "start with Gloat" to rapidly build sophisticated AI capabilities without waiting for their legacy HR vendors to develop comparable functionalities. The open nature of the Gloat platform also facilitates integration with other internal systems, enhancing its flexibility. Furthermore, Gloat’s proprietary context engine, Loomra, appears to offer a unique advantage, as the author is not aware of any comparable systems developed by other major HCM vendors.
However, the market for HR agent tools is exceptionally competitive. Organizations deeply invested in specific ecosystems will likely gravitate towards solutions offered by their primary vendors. For example, ServiceNow customers may favor ServiceNow’s agent capabilities, Workday clients will lean towards Sana, Oracle users will likely adopt Oracle AI Studio, and SAP customers will explore Joule Studio. Workday, in particular, has recently articulated a comprehensive agent strategy, emphasizing its commitment to this domain.
Gloat’s success will depend on its ability to demonstrate to potential clients that its tools are not only easier to use and fully integrated but also demonstrably more context-aware than competing solutions. As the enterprise technology landscape transitions from "systems of record" to "systems of context," Gloat’s emphasis on deep workforce understanding could position it as a leader in this evolving paradigm.
Looking Ahead: The Future of Enterprise AI
The evolution of application development tools has historically followed a pattern where new platforms emerge, and companies tend to adopt toolsets that align with their existing technological infrastructure. The emergence of platforms like Dreamweaver, PowerBuilder, Cold Fusion, and more recently, Anthropic’s Cowork and Microsoft’s GitHub Copilot, illustrates this trend.
Gloat’s new Agentic HR platform represents an innovative leap forward in the agent industry. The company’s ability to abstract complex business rules and provide a rich contextual layer for AI agents is a significant development. As Gloat begins to onboard early customers and gather real-world implementation stories, its impact on the future of HR technology and the broader enterprise AI landscape will become clearer. The ongoing dialogue around agents, superagents, and intelligent orchestration will undoubtedly continue to shape the strategic imperatives for enterprise AI in the coming years.
