This week, Gloat, a prominent player in skills intelligence and talent marketplace solutions, has made a significant strategic move by entering the burgeoning field of AI Agents for Human Resources. This bold entry signals a new era of competition for core HR technology, as established companies and innovative startups alike vie for dominance in this rapidly evolving sector. Gloat’s offering, termed "Gloat Agentic HR," provides a comprehensive toolset designed to leverage existing business rules and security protocols within major HR systems like Oracle, Workday, and SuccessFactors. This capability allows organizations to rapidly develop and deploy AI Agents directly within popular communication and collaboration platforms such as Microsoft Copilot, Teams, and Slack.
The Evolving Architecture of Agentic AI in HR
To understand Gloat’s strategic positioning, it is crucial to examine the layered architecture of agentic AI. At the foundational level reside the "systems of record," which are the robust Human Capital Management (HCM) and Enterprise Resource Planning (ERP) applications that store, update, and maintain critical organizational data. These include behemoths like Workday, SAP, Oracle, and UKG, which manage everything from financial records and customer information to employee details and inventory.
Layered above these core systems are "cross-system applications." Given that no single vendor offers a complete solution, companies have developed extensive ecosystems of specialized applications. These range from employee portals and mobile apps to hundreds of niche tools for time tracking, learning management (LMS), applicant tracking (ATS), and IT provisioning. Industry analyses suggest that a large enterprise typically utilizes around 400 such applications, with over 100 directly impacting the employee experience.
Over the past two decades, as organizations transitioned from on-premise infrastructure to cloud-based solutions, this application ecosystem has flourished. Companies like ServiceNow, a $13.5 billion enterprise software company experiencing 20% annual growth, have emerged as dominant players in managing these cross-system workflows for global organizations. Furthermore, employee experience platforms such as Microsoft Viva, Slack, Google Workspace, and dedicated tools like Workvivo (Zoom), Firstup, and Staffbase have become commonplace. However, many of these platforms were not initially designed with "agentic" capabilities in mind, meaning they were not built to proactively act on behalf of users or perform complex tasks autonomously.
The third layer represents the new generation of AI Agents. These agents transcend the functionality of mere portals or workflow tools. They possess an understanding of the individual user and can execute actions on their behalf. The HR technology landscape is now seeing a proliferation of these agent tools, originating from both small, agile startups and integrated offerings from larger enterprise software providers. These agents aim to facilitate the creation of cross-functional solutions that enhance people management processes.
Crowning this architecture are "Superagents." These advanced tools are designed to orchestrate and access multiple functional agents, creating a seamless and intuitive "walk-up-and-use" experience for employees. This layered approach provides a framework for understanding how AI agents are being integrated into the complex fabric of enterprise operations.
The Competitive Landscape of Agent Creation Tools
The current market for agent creation tools is exceptionally dynamic and crowded. Notable players include Sana (integrated with Workday), Oracle’s Agent Studio, Microsoft Copilot Studio, Leena.ai, and ServiceNow, which is further bolstering its agent capabilities through recent acquisitions like MoveWorks. SAP is also actively participating with its Joule Studio, alongside foundational AI providers such as Anthropic, OpenAI, and Google, whose underlying technologies power many of these specialized solutions.
One notable example of a "Superagent" designed to act as an advisor or HR professional is Galileo. This platform empowers employees to ask questions, receive immediate answers, and access the underlying agents for more complex actions, streamlining HR interactions.
Navigating the Complexities of Agent Implementation
While the promise of AI agents is compelling, their effective implementation hinges on navigating the intricate web of existing business rules, security protocols, and legacy systems. For instance, a payroll reconciliation agent from Workday, SAP, or Oracle, designed to monitor payroll transactions and reconcile tax information, employee movements, and pay adjustments, cannot function effectively without being meticulously "trained" with a company’s specific rules and security parameters. Furthermore, to address more sophisticated requirements like pay equity monitoring or performance-based adjustments, these agents must seamlessly integrate and communicate with other specialized agents.
This necessity underscores the critical importance of "agent architecture." Consider a scenario where an organization aims to redeploy 5,000 employees from an existing business unit to new roles. An agent tasked with identifying suitable candidates, assessing skill gaps, and facilitating transfers would need to interact with a multitude of other agents. Such an agent would require a deep understanding of job requirements, skill adjacencies, and potential career trajectories. It would also need to provide guidance to employees and managers on available options, identify relevant training programs, and account for logistical factors like geographic location, job licensing, union agreements, and compensation structures.
The complexity of these interwoven dependencies is significant. Common use cases for such sophisticated agent applications span global onboarding, promotion processes, annual performance and compensation reviews, talent acquisition, employee certification management, and intricate time and schedule management.
The Strategic Battleground: Business Rules and Semantic Layers
The race to develop and deploy effective HR agents has ignited a fierce competition among enterprise software vendors. While many offer specialized coaching tools, the true challenge lies not just in building individual agents, but in their seamless integration and orchestration. This integration directly confronts the critical issue of how to manage an organization’s unique business rules, security policies, existing workflows, and proprietary "business objects." A business object, in this context, could refer to a company’s specific career framework, performance review methodology, or compensation philosophy.
These deeply embedded rules and rubrics represent the core intellectual property and competitive advantage of a company. As business strategies evolve, so too must these underlying operational frameworks. The goal is to avoid hard-coding these dynamic rules directly into agents, which would render them inflexible and prone to obsolescence. Instead, agents need to access a centralized "semantic layer" that accurately reflects and maintains this crucial business information.
Leading HCM vendors, including Workday, Oracle, and SAP, already possess sophisticated semantic layers through their proprietary business process frameworks. They are actively integrating these capabilities into their agent development tools. Consequently, any HR agent or agent development platform will inevitably seek to connect with this vital semantic layer. This dynamic may even challenge predictions of a "SaaSpocalypse," suggesting a continued reliance on integrated, data-rich platforms.
Gloat’s Strategic Intervention: Loomra and Agentic HR
Gloat’s recent launch aims to liberate organizations from the constraints of traditional, vendor-locked approaches to AI agent development. The company has developed an "agent-driven auto-discovery ‘injector’" named Loomra. This sophisticated technology mines and replicates an organization’s unique entities, workflows, and business rules from its existing HCM systems, and crucially, maintains synchronization as these systems evolve. Loomra effectively creates a mirrored semantic layer, enabling the effortless development of applications and agents that are intrinsically aligned with an organization’s operational logic.
Gloat then provides an intuitive agent builder, allowing users to visually construct their own agents. These custom agents can be directly integrated into widely used platforms like Microsoft Teams, Copilot, and Slack, streamlining accessibility and user adoption. The company has already pre-built a suite of agents addressing critical HR functions where it possesses deep expertise, including Workforce Redeployment, Career Development, Internal Talent Sourcing, Succession Planning, and Learning & Reskilling.
The Differentiating Power of Workforce Context
What sets Gloat’s Agentic HR platform apart is the underlying "workforce context layer" powered by Loomra. This engine is built upon nearly a decade of enterprise-scale workforce data, meticulously modeling skill adjacencies, career trajectories, organizational patterns, and inter-employee relationships across millions of individuals globally.
This robust foundation empowers Gloat’s agents to move beyond simple information retrieval. Instead of merely responding to queries like "show me employees who know Python," these agents can engage in sophisticated 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 as business priorities shift.
A Comparative Analysis in a Competitive Market
Gloat’s approach presents a compelling proposition: organizations can potentially "start with Gloat" to build custom AI capabilities on top of their existing HCM infrastructure, without waiting for their legacy vendors to develop comparable functionalities. The open nature of the Gloat platform further facilitates integration with other internal systems. Furthermore, Gloat’s unique context engine, Loomra, appears to be a significant differentiator, as few other HCM vendors have publicly announced similar comprehensive workforce context modeling capabilities.
However, the agent tools market is intensely competitive. Organizations deeply invested in specific HCM ecosystems are likely to gravitate towards the native agent development solutions offered by their providers. Workday customers may opt for Sana’s offerings, Oracle users for Oracle AI Studio, and SAP clients for Joule Studio. The success of Gloat will depend on its ability to demonstrate that its tools are not only easier to use and fully integrated but also offer a superior level of context-awareness compared to alternatives. As the corporate world increasingly shifts from "systems of record" to "systems of context," Gloat’s innovative approach could position it as a leader in this transformative wave.
Future Outlook and Industry Implications
The evolution of application development tools has consistently shown a pattern where companies adopt solutions that align with their existing technological infrastructure. Gloat’s new platform represents a significant innovation, pushing the boundaries of the agent industry forward. The company’s ability to effectively mine and replicate complex business rules and semantic layers from diverse HCM systems, coupled with its deep workforce context capabilities, offers a unique value proposition.
As Gloat begins to onboard its early customers, their experiences and feedback will be crucial in assessing the real-world impact and adoption rates of its Agentic HR platform. The coming months will reveal how effectively Gloat can carve out its niche in a fiercely competitive market, potentially reshaping how organizations leverage AI for their human capital management strategies. The focus on "systems of context" as a foundational element for intelligent automation suggests a significant shift in how businesses will operate and manage their most valuable asset: their people.
