This week, Gloat, a prominent player in skills intelligence and talent marketplace solutions, has made a significant stride into the burgeoning field of AI Agents for Human Resources. The company’s launch of Gloat Agentic HR signals a new wave of competition for core HR technology, demonstrating a strategic pivot towards leveraging artificial intelligence to streamline and enhance HR functions. This move by Gloat positions it as a key contender in a rapidly evolving market where established HR tech giants and agile startups are vying for dominance.
The Rise of Agentic AI in HR
The introduction of Gloat Agentic HR is underpinned by a sophisticated architectural approach to AI agents. At its core, Gloat’s offering provides a robust toolset designed to integrate seamlessly with existing enterprise resource planning (ERP) and human capital management (HCM) systems, such as Oracle, Workday, and SuccessFactors. This integration allows organizations to rapidly develop and deploy AI Agents within popular communication and collaboration platforms like Microsoft Copilot, Teams, and Slack. These agents are capable of executing complex HR tasks by leveraging the established business rules and security protocols already in place within these foundational systems.
To understand Gloat’s strategic positioning, it’s crucial to examine the layered architecture of agentic AI as it applies to enterprise operations, particularly within HR. This framework typically comprises five distinct layers:
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Systems of Record: At the base of this architecture lie the foundational systems that store, update, and maintain critical organizational data. These include major HCM applications like Workday, SAP, Oracle, and UKG, which sit atop extensive databases housing information on financials, customers, employees, inventory, and products. These systems serve as the authoritative source of truth for an organization’s operational data.
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Cross-System Applications and Employee Experience Platforms: Layered above the systems of record are applications designed to bridge the gaps between disparate systems and enhance the employee experience. In large enterprises, a complex ecosystem of hundreds of specialized applications, such as time-tracking systems, learning management systems (LMS), applicant tracking systems (ATS), and IT provisioning tools, often coexist. Employee-facing portals, mobile applications, and workflows are built to traverse these systems. Major players like ServiceNow, a company valued at over $13.5 billion with a reported 20% annual growth rate, have historically dominated this layer for global organizations. More recently, employee experience platforms like Microsoft Viva, Zoom’s Workvivo, Firstup, and Staffbase, along with tools built on Google and Slack, have gained traction. However, many of these platforms were not initially designed with "agentic" capabilities in mind.
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Agent Layer: This is the domain of the new breed of AI Agents. Unlike traditional portals or workflow tools, these agents possess intelligence about individual users and can act on their behalf. The HR technology landscape is now experiencing an influx of hundreds of agent tools, ranging from those offered by emerging startups to those embedded within enterprise systems. These agents aim to facilitate the creation of cross-functional solutions for managing and engaging with a company’s workforce.
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Superagents: At the apex of this architecture are "Superagents." These advanced tools are designed to orchestrate and access functional agents, providing users with a highly intuitive and seamless "walk-up-and-use" experience. They aggregate the capabilities of multiple underlying agents to deliver comprehensive solutions.
This multi-layered approach has led to a proliferation of tools. The market is now populated by agent development studios from major vendors like Oracle (Oracle Agent Studio), SAP (Joule Studio), and Microsoft (Microsoft Copilot Studio), as well as specialized solutions from companies like Sana (integrating with Workday), Leena.ai, and ServiceNow, which has recently bolstered its capabilities through the acquisition of MoveWorks. Underlying these are the foundational AI models from industry leaders such as Anthropic, OpenAI, and Google.
The Mechanics of Agentic HR: Bridging Legacy and Innovation
While the potential of agentic AI in HR is immense, its practical implementation is significantly influenced by the complexities of existing business rules, security frameworks, and legacy systems. For instance, a payroll reconciliation agent developed by a major vendor might monitor payroll transactions, including tax calculations, timecard adjustments, employee status changes (new hires, terminations), and pay adjustments. However, its effectiveness is contingent upon its ability to be "trained" with an organization’s specific rules and security parameters. Furthermore, for an agent to perform more advanced functions, such as monitoring pay equity or facilitating performance-based adjustments, it must be able to communicate and integrate with other agents and systems.
This necessity underscores the importance of a well-defined "agent architecture." Consider a scenario where an organization aims to redeploy 5,000 employees from a declining business unit to emerging roles. An agent tasked with identifying suitable candidates would need to analyze job fit, assess skill gaps, and evaluate potential for internal mobility before any large-scale layoffs or new hires are initiated. Such a sophisticated agent would require access to and communication with numerous other agents and data sources.
A hypothetical agent designed for this purpose, as might be developed within a platform like Galileo, would need to possess a deep understanding of skill and job requirements. It would also need to provide coaching to both employees and managers regarding their career options. This "Superagent" should also be aware of available training programs, geographical considerations, licensing requirements, union agreements, and prevailing pay scales, among other factors. The inherent complexity of such an endeavor highlights the intricate nature of building truly effective HR agents.
Beyond workforce redeployment, common use cases for agentic AI in HR include global onboarding processes, managing internal promotions, streamlining annual reviews and compensation cycles, and optimizing the entire talent acquisition pipeline, employee certification tracking, and time and schedule management.
The Strategic Battleground: Enterprise Agents and Business Rules
The race to develop and deploy AI agents for HR is intensifying, with nearly every major HR technology vendor actively building out their agent capabilities. While some offerings focus on basic coaching functions, others delve into more complex operational areas. Leading vendors, particularly Workday, Oracle, SAP, and ServiceNow, are investing heavily in AI studios – platforms designed to simplify the creation and deployment of these agents.
However, the critical challenge extends beyond merely acquiring or building individual agents. The true competitive battle lies in the ability to effectively "stitch together" these agents into cohesive and functional workflows. This integration process is deeply intertwined with an organization’s specific business rules, existing security frameworks, established workflows, and proprietary business "objects." These objects can encompass a wide range of assets, such as a company’s defined career framework or its performance review methodology.
These internal rules and rubrics are not static; they represent the core value and operational model of a business and evolve alongside organizational changes. The objective is to avoid hardcoding these dynamic rules directly into agents. Instead, agents should be able to access a "semantic layer" that accurately maintains and reflects this crucial business information.
Major HCM vendors like Workday, Oracle, SAP, and UKG inherently possess robust semantic layers, exemplified by Workday’s Business Process Framework. These vendors are increasingly integrating these layers into their agent development tools. Consequently, any HR agent or agent development platform will seek to leverage this foundational layer of contextual information. This trend may also challenge earlier predictions of a "SaaSpocalypse," suggesting a more integrated future for SaaS solutions.
Gloat’s Differentiated Approach: Loomra and Agentic HR
Gloat’s new Agentic HR platform is designed to liberate organizations from the constraints of traditional, legacy approaches. The company has developed an "agent-driven auto-discovery ‘injector’" named Loomra. This innovative technology mines an organization’s existing HCM system to identify and replicate key entities, workflows, and business rules. Crucially, Loomra continuously synchronizes this extracted information with the HCM system, ensuring that the replicated rules and objects remain up-to-date as the underlying HCM evolves. This capability allows for the efficient development of custom applications and agents built directly on top of an organization’s precise business logic.
Following the establishment of this semantic layer, Gloat provides an intuitive agent builder. This visual tool enables users to construct their own agents, which can then be directly integrated into platforms such as Microsoft Teams, Copilot, and Slack. Gloat has already developed pre-built agents targeting key HR areas where it possesses deep expertise, including Workforce Redeployment, Career Development, Internal Talent Sourcing, Succession Planning, and Learning & Reskilling.
The Power of Workforce Context
What distinguishes Gloat’s Agentic HR platform is the robust workforce context layer that underpins it. Gloat’s Workforce Context Engine, Loomra, is built upon nearly a decade of experience in collecting and modeling enterprise-scale workforce data. This engine captures intricate details such as skill adjacencies, typical career trajectories, organizational patterns, and workforce relationships across millions of employees globally.
This comprehensive contextual understanding empowers Gloat’s agents to move beyond simple information retrieval. Instead of merely responding to a query like "show me employees who know Python," these agents can perform sophisticated reasoning. They can identify which employees are best positioned to transition into AI engineering roles within the current year, pinpoint teams most vulnerable to future skill gaps, and recommend optimal talent redeployment strategies in response to shifting business priorities.
Competitive Landscape and Strategic Implications
Gloat’s offering presents a compelling proposition: organizations can potentially "start with Gloat" to build advanced HR capabilities on top of their existing HCM infrastructure, without waiting for their legacy vendors to develop comparable solutions. The open nature of the Gloat platform also facilitates the integration of agents with other internal systems. Furthermore, Gloat’s proprietary context engine, Loomra, appears to be a unique differentiator, as current HCM vendors have not yet publicly disclosed comparable systems.
However, the market for agent tools is intensely competitive. Organizations heavily invested in specific ecosystems will likely gravitate towards native solutions: ServiceNow users will continue to leverage ServiceNow’s agent capabilities, Workday customers are expected to adopt Sana, Oracle customers will utilize Oracle AI Studio, and SAP customers will engage with Joule Studio.
Gloat’s success hinges on its ability to demonstrate that its tools offer superior ease of use, seamless integration with diverse HCM systems, and a more profound level of contextual intelligence than competing solutions. As the enterprise technology landscape transitions from "systems of record" to "systems of context," Gloat’s strategic focus on deep workforce context could position it as a leader in this transformative era.
Future Outlook
The evolution of application development tools has historically seen new platforms emerge, with companies typically selecting toolsets that align with their existing infrastructure. Gloat’s new platform represents an innovative advancement that pushes the boundaries of the agent industry. As early customer implementations begin to yield insights, the true impact and adoption trajectory of Gloat Agentic HR will become clearer. The company’s ability to effectively communicate its unique value proposition in terms of ease of integration, contextual depth, and speed to deployment will be critical in navigating the highly competitive landscape of AI-driven HR technology. The industry will be closely watching how Gloat’s innovative approach shapes the future of HR operations.
