In a significant development that underscores the rapidly evolving landscape of human resources technology, skills intelligence leader Gloat has announced its strategic entry into the realm of AI Agents for HR. This move signifies a burgeoning competition for core HR technology solutions, with established players and innovative startups vying to integrate artificial intelligence into the employee lifecycle. Gloat’s offering, branded as Gloat Agentic HR, aims to empower organizations to rapidly develop and deploy AI agents across popular communication and collaboration platforms like Microsoft Copilot, Teams, and Slack.
The core innovation of Gloat Agentic HR lies in its ability to leverage existing business rules and security protocols embedded within major Human Capital Management (HCM) systems, such as Oracle, Workday, and SuccessFactors. This approach allows businesses to build sophisticated AI agents without necessitating a complete overhaul of their current infrastructure, a critical consideration for large enterprises with complex and deeply integrated systems.
Understanding the Architecture of Agentic AI in HR
To fully grasp the significance of Gloat’s announcement, it is essential to understand the layered architecture of agentic AI as it applies to the HR domain. This framework provides a crucial context for the challenges and opportunities presented by AI in the modern workplace.
Layer 1: Systems of Record
At the foundational level lie the "systems of record." These are the robust databases and applications that meticulously store, update, and maintain an organization’s most critical information. This includes financial data, customer details, employee records, inventory management, and product information. Prominent examples in this category include enterprise resource planning (ERP) systems like Workday, SAP, Oracle, and UKG. These systems form the bedrock of corporate operations, housing the raw data upon which all other applications and processes depend.
Layer 2: Cross-System Applications and Employee Experience Platforms
Building upon these systems of record is a layer of "cross-system applications." Recognizing that no single vendor can comprehensively address every business need, companies often develop or integrate a multitude of specialized applications. These can range from employee portals, mobile applications, and custom workflows that traverse multiple systems, to highly specific tools like learning management systems (LMS), applicant tracking systems (ATS), and IT provisioning software. It is estimated that a large enterprise can utilize an average of 400 such applications, with over 100 directly impacting employee experience.
Over the past two decades, as organizations migrated from on-premises solutions to cloud-based services, an intricate ecosystem of these applications has emerged. Companies like ServiceNow, a multi-billion dollar enterprise service management provider, have become dominant players in this layer, facilitating the integration and management of these diverse tools. Furthermore, the proliferation of employee experience platforms, including Microsoft Viva, Zoom’s Workvivo, Firstup, and Staffbase, along with tools built on Google and Slack, has further enriched this layer. However, many of these platforms were not originally designed with "agentic" capabilities in mind.
Layer 3: The Emergence of AI Agents
The third layer represents the new breed of AI Agents. These are fundamentally more advanced than traditional portals or workflow tools. AI agents are designed to possess intelligence about individual users and can proactively perform tasks on their behalf. In the HR sector, this has led to an explosion of agent-based tools, often pioneered by agile startups, as well as embedded capabilities within larger enterprise systems. These agents are designed to facilitate the creation of cross-functional solutions that directly address people-related challenges.
Layer 4: Superagents and Unified Experiences
Crowning this architecture are "Superagents." These advanced tools are capable of orchestrating and accessing multiple functional agents, providing users with a seamless and intuitive "walk-up-and-use" experience. This layered approach highlights the complexity of integrating AI into the intricate fabric of enterprise HR systems.
The current market is witnessing a surge of tools operating within this agentic AI framework. This includes offerings like Sana from Workday, Oracle Agent Studio, Microsoft Copilot Studio, Leena.ai, and new agent capabilities from ServiceNow, bolstered by its acquisition of MoveWorks. SAP’s Joule Studio, alongside foundational AI models from Anthropic, OpenAI, and Google, also contribute to this dynamic ecosystem. Tools like Galileo, for instance, are positioned as advisory agents, empowering employees to seek information and access underlying agent functionalities.
The Operationalization of AI Agents: Navigating Complexity
While the potential of AI agents is immense, their practical implementation is fraught with challenges, primarily revolving around business rules, security protocols, and the integration with legacy systems.
Consider a payroll reconciliation agent offered by a major HCM vendor. Such an agent is designed to monitor payroll transactions, reconciling tax liabilities, time-card data, employee status changes (hires, terminations), and pay adjustments. However, its efficacy is contingent upon its ability to be "trained" with a company’s specific business rules and security configurations. Furthermore, to address more sophisticated use cases like pay equity monitoring or performance-based adjustments, it must be capable of inter-agent communication.
This underscores the critical importance of "agent architecture." Imagine a scenario where an organization needs to redeploy 5,000 employees from an existing business unit to new roles. An AI agent tasked with identifying suitable candidates would need to analyze job fit, assess skill gaps, and evaluate potential for transition. This agent would require seamless integration with multiple other agents to access data on required skills, available training programs, geographical constraints, licensing requirements, union regulations, and compensation benchmarks.
The development of such sophisticated "Superagents" is a complex undertaking. Gloat’s own experience with Galileo, a platform designed to function as an HR professional agent for employees, demonstrates this complexity. Galileo aims to provide employees with a self-service mechanism for asking questions, receiving answers, and accessing relevant agent functionalities, all while understanding nuanced factors like skill requirements, career trajectories, and available development resources.
Beyond workforce redeployment, common applications for agentic AI in HR include global onboarding processes, managing promotions and annual reviews, optimizing compensation strategies, streamlining talent acquisition, ensuring employee certifications, and managing complex time and schedule administration.
The Competitive Arena: The Battle for Enterprise Agents and Business Logic
Every major HR technology vendor is actively developing AI agents to enhance their offerings. These range from simple coaching tools to more comprehensive solutions. Leading vendors, particularly Workday, Oracle, SAP, and ServiceNow, are investing heavily in AI studios, aiming to simplify the agent development process for their clientele.
However, the primary battleground is not merely the selection or development of individual agents, but the ability to seamlessly integrate them into a cohesive operational framework. This integration hinges on the effective management of business rules, security policies, existing workflows, and proprietary business "objects"—such as an organization’s unique career framework or performance review model.
These business rules and frameworks represent the core intellectual property and operational logic of a company. As businesses evolve, so too must these rules. The goal is to avoid hard-coding these dynamic elements directly into agents, instead enabling agents to access a "semantic layer" that dynamically maintains this crucial information.
Major HCM vendors like Workday, Oracle, SAP, and UKG inherently possess robust semantic layers, such as Workday’s Business Process Framework. They are now integrating these foundational components into their agent development tools. This implies that any HR agent or agent development platform will need to interface with this vital layer. This trend could potentially challenge the notion of a "SaaSpocalypse," where a single vendor dominates all aspects of enterprise software.
Gloat’s Strategic Gambit: Loomra and Agentic HR
Gloat aims to liberate organizations from the constraints of traditional, often rigid, legacy approaches. Their newly launched Agentic HR platform introduces "Loomra," an agent-driven auto-discovery and ingestion engine. Loomra is designed to systematically mine an organization’s HCM system for entities, workflows, and business rules, ensuring that this knowledge base remains synchronized with ongoing system updates. Essentially, Loomra replicates the critical rules and objects resident in an HCM system, providing a foundation upon which custom applications and agents can be built with greater ease.
Gloat further facilitates agent creation through its visual builder, allowing users to construct bespoke agents that integrate directly into platforms like Microsoft Teams, Copilot, and Slack. The company has already developed pre-built agents for key HR functions where it possesses established expertise, including Workforce Redeployment, Career Development, Internal Talent Sourcing, Succession Planning, and Learning & Reskilling.
Workforce Context as a Differentiating Factor
What distinguishes Gloat’s Agentic HR platform is its underlying "workforce context layer." Loomra, Gloat’s Workforce Context Engine, is built upon nearly a decade of accumulating and modeling enterprise-scale workforce data. This includes sophisticated analysis of skill adjacencies, career trajectory patterns, organizational structures, and workforce relationships across millions of employees globally.
This robust contextual understanding empowers Gloat’s agents to move beyond simple information retrieval. Instead of merely responding to queries like "show me employees who know Python," agents can perform complex reasoning. They can identify employees with the potential to transition into AI engineering roles within the year, pinpoint teams most vulnerable to future skill gaps, and recommend optimal talent redeployment strategies aligned with shifting business priorities.
Comparative Analysis: Gloat in a Crowded Market
Gloat’s approach presents a compelling proposition: enabling organizations to "start with Gloat" and build upon their existing HCM infrastructure, bypassing the often lengthy development cycles of legacy vendors. The platform’s open architecture also facilitates integration with other internal systems. Moreover, Gloat’s unique context engine, Loomra, is a significant differentiator, as few, if any, other HCM vendors have developed a comparable system.
However, the market for agent tools is intensely competitive. Organizations deeply invested in specific HCM ecosystems are likely to gravitate towards native solutions. Workday customers may opt for Sana, Oracle users for Oracle AI Studio, and SAP users for Joule Studio. Each of these platforms offers a compelling vision for AI integration within their respective ecosystems.
Gloat’s challenge lies in convincing potential customers that its tools are not only easier to use and seamlessly integrated but also offer superior context-awareness compared to competing solutions. As the enterprise IT landscape shifts from "systems of record" to "systems of context," Gloat has the potential to emerge as a leader, provided it can effectively articulate and demonstrate this advantage.
Future Outlook: The Evolving Dynamics of Enterprise AI
The evolution of application development tools has historically followed patterns where new platforms emerge, and organizations adopt those that best align with their existing infrastructure and strategic objectives. Gloat’s new Agentic HR platform represents an innovative leap forward for the AI agent industry. The company’s ability to bridge the gap between the structured data in HCM systems and the dynamic, contextual understanding required for advanced AI applications could prove to be a significant factor in its success.
Industry analysts will be closely monitoring the adoption and performance of Gloat’s platform with its early customers. The ensuing case studies and testimonials will provide valuable insights into the practical implications and long-term impact of this new wave of agentic AI in the HR domain. The ongoing competition among established HCM vendors and agile innovators like Gloat promises to accelerate the development and deployment of intelligent HR solutions, ultimately reshaping how organizations manage and empower their workforces.
