The human resources technology landscape is undergoing a profound transformation, marked by the emergence of AI agents designed to streamline and enhance HR functions. This evolution is underscored by Gloat’s recent, significant entry into this burgeoning market with its Gloat Agentic HR platform. The move signals a new wave of competition for core HR technology, as companies like Gloat aim to leverage artificial intelligence to connect with and empower employees through familiar communication channels.
Gloat’s offering, Gloat Agentic HR, is positioned as a powerful toolset that bridges the gap between robust enterprise HR systems and the everyday digital workspaces employees utilize. By integrating with established platforms such as Oracle, Workday, and SuccessFactors, Gloat’s solution allows for the rapid development of AI agents. These agents are designed to operate seamlessly within Microsoft Copilot, Teams, Slack, and other popular AI-driven applications, promising a more intuitive and accessible HR experience. This strategic positioning highlights a critical trend: the migration of HR functionalities from monolithic systems to more agile, user-centric interfaces powered by AI.
The Evolving Architecture of AI Agents in HR
To fully appreciate Gloat’s innovation, it’s essential to understand the underlying architectural layers that define the current AI agent ecosystem. This framework, often visualized as a multi-tiered structure, helps explain how data flows and how agents interact with various systems.
At the foundational level are the Systems of Record. These are the bedrock of any enterprise, housing critical data about employees, financials, customers, and operations. Prominent examples include Human Capital Management (HCM) systems like Workday, SAP, Oracle, and UKG, alongside broader Enterprise Resource Planning (ERP) systems. These systems are the primary custodians of organizational information, maintaining and updating it through complex databases.
Layered above these systems are Cross-System Applications and Portals. In large organizations, no single vendor provides all necessary functionalities. Consequently, companies develop intricate ecosystems of specialized applications for functions such as time tracking, learning management (LMS), applicant tracking (ATS), and IT provisioning. The average large enterprise manages hundreds of such applications, with a significant portion directly impacting employee experience. Over the past two decades, the migration of these systems to the cloud has fostered a rich ecosystem of solutions. ServiceNow, a company valued at $13.5 billion and experiencing 20% annual growth, has emerged as a dominant player in managing this layer for global corporations. Employee experience platforms, including Microsoft Viva, Zoom’s Workvivo, Firstup, and Staffbase, alongside tools built on Google and Slack, are also prevalent. However, many of these were not originally designed with "agentic" capabilities in mind.
The third and most dynamic layer is the realm of AI Agents. These are distinct from simple portals or workflows; they possess intelligence about individual users and can proactively perform tasks on their behalf. The HR technology market is currently witnessing an influx of agent tools, ranging from those developed by agile startups to capabilities embedded within enterprise systems. These agents aim to facilitate the creation of cross-functional solutions tailored to people management.
Crowning this architecture are Superagents. These advanced tools are designed to orchestrate and access multiple functional agents, creating a cohesive and intuitive "walk-up-and-use" experience for end-users. This layered approach provides a comprehensive view of how AI agents are being integrated into the broader enterprise technology stack.
The current market for these agent-building tools is highly competitive. Notable platforms include Sana (from Workday), Oracle Agent Studio, Microsoft Copilot Studio, Leena.ai, and SAP’s Joule Studio. ServiceNow is also expanding its agent capabilities, notably through its acquisition of MoveWorks. Furthermore, foundational AI providers like Anthropic, OpenAI, and Google are contributing to this landscape with their advanced language models and agent development frameworks.
The Mechanics of Agentic HR: Bridging Data and Action
The promise of AI agents in HR is considerable, but their practical implementation hinges on navigating complex business rules, security protocols, and existing legacy systems. For instance, a payroll reconciliation agent, whether offered by Workday, SAP, or Oracle, requires extensive "training" with a company’s specific rules and security parameters to function effectively. Such an agent would typically monitor payroll transactions, reconciling data related to taxes, timecards, employee status changes (new hires, terminations), and pay adjustments. However, its utility is contingent on its ability to integrate with other systems and agents to address broader concerns like pay equity or performance-based adjustments.
This highlights the critical importance of agent architecture. Consider a scenario where an organization aims to redeploy 5,000 employees from declining business units to emerging roles. An AI agent designed for this purpose would need to analyze job fit, identify skill gaps, and assess redeployment potential before initiating mass layoffs or hiring for new positions. Such a sophisticated agent would necessitate seamless communication with multiple other agents across the organization’s systems.
An example of such a "Superagent" can be found in platforms like Galileo. This type of agent acts as an advisor or HR professional, enabling employees to ask questions, receive answers, and access underlying HR functionalities. To effectively manage a large-scale redeployment, a Superagent would need to understand intricate skill and job requirements, provide guidance to employees and managers on available options, and possess knowledge of training resources, geographical constraints, licensing requirements, union agreements, and compensation structures. The complexity involved underscores the need for robust and interconnected agent systems.
Beyond workforce redeployment, common use cases for these advanced agents span a wide range of HR processes, including global onboarding, internal mobility and promotions, annual performance reviews and compensation cycles, talent acquisition, employee certification management, and time and schedule optimization.
The Strategic Battleground: Securing and Integrating Business Rules
The proliferation of AI agents presents a significant opportunity for HR vendors, with many developing tools ranging from simple coaching assistants to highly sophisticated operational agents. Major enterprise software providers, including Workday, Oracle, SAP, and ServiceNow, are actively developing AI studios to empower their clients in building and deploying these agents.
However, the central challenge and the arena for intense competition lie not merely in the acquisition or development of individual agents, but in their seamless integration and orchestration. This integration process is intrinsically linked to the management of business rules, security policies, existing workflows, and proprietary business "objects"—such as career frameworks or performance review models—that are unique to each company.
These rules and rubrics represent the core value and operational model of an organization. As businesses evolve, these internal frameworks must adapt. The goal is to avoid hardcoding these dynamic rules directly into agents, instead enabling agents to access and leverage a "semantic layer" that stores and manages this critical information.
Established HCM vendors, with their existing semantic layers (e.g., Workday’s Business Process Framework), are integrating these capabilities into their agent development tools. This positions them to offer agents that are deeply embedded within their platforms. Consequently, any new HR agent or agent development tool is likely to seek access to this vital semantic layer. This dynamic may even challenge predictions of a "SaaSpocalypse," suggesting a more intertwined future for SaaS solutions.
Gloat’s Strategic Play: Loomra and Agentic HR
Gloat’s latest initiative is designed to offer a departure from the traditional reliance on legacy approaches. The company has introduced an "agent-driven auto-discovery ‘injector’" named Loomra. This sophisticated component is engineered to mine entities, workflows, and business rules from existing HCM systems, ensuring these are kept synchronized as the core system evolves. Essentially, Loomra replicates the intricate rules and business objects of an HCM system, creating a foundation upon which new applications and agents can be readily built.
Following the deployment of Loomra, Gloat provides an intuitive agent builder. This visual tool allows organizations to construct their own AI agents, which can then be deployed directly into familiar platforms such as Microsoft Teams, Copilot, and Slack. Gloat has already developed pre-built agents for critical HR functions where it possesses significant expertise, including Workforce Redeployment, Career Development, Internal Talent Sourcing, Succession Planning, and Learning & Reskilling.
The Power of Workforce Context
What truly differentiates Gloat’s Agentic HR platform is the underlying workforce context layer powered by Loomra. This engine is built upon nearly a decade of experience in analyzing enterprise-scale workforce data. It models skill adjacencies, career trajectories, organizational patterns, and complex workforce relationships across millions of employees globally.
This rich contextual understanding enables Gloat’s agents to transcend 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 employees with the potential to transition into AI engineering roles within the current year, pinpoint teams most vulnerable to future skill gaps, and recommend talent redeployment strategies aligned with shifting business priorities. This deep contextual awareness is a significant advantage in a market increasingly focused on predictive and proactive HR solutions.
Competitive Landscape and Gloat’s Positioning
Gloat’s approach offers a compelling proposition: organizations can potentially "start with Gloat" to rapidly deploy AI-powered HR solutions without waiting for their existing HCM vendors to develop equivalent capabilities. The open nature of the Gloat platform also facilitates integration with other internal systems. Furthermore, Gloat’s context engine, Loomra, appears to be a unique offering, with no other major HCM vendors known to have developed a comparable system to date.
However, the market for AI agent tools is fiercely competitive. Organizations deeply invested in specific ecosystems will likely gravitate towards solutions from their primary vendors: ServiceNow users will favor ServiceNow’s offerings, Workday customers will adopt Sana, Oracle clients will leverage Oracle AI Studio, and SAP users will utilize Joule Studio.
Gloat’s challenge will be to persuade 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 corporate world shifts from "systems of record" to "systems of context," Gloat has the potential to emerge as a leader, provided it can effectively communicate its unique value proposition and deliver on its promises.
The Future of HR Agents: A Dynamic Evolution
The evolution of application development tools, from early web development platforms to the current wave of AI-powered frameworks like Anthropic’s Cowork and Microsoft’s GitHub Copilot, suggests a pattern: companies tend to adopt toolsets that align with their existing infrastructure and strategic priorities.
Gloat’s new platform represents an innovative step forward for the agent industry, pushing the boundaries of what’s possible in HR technology. The success of this initiative will likely depend on its ability to resonate with enterprise needs for agile, context-aware, and user-friendly AI solutions. As early customer adoption and real-world use cases emerge, the impact of Gloat’s entry into the AI agent space will become clearer, potentially reshaping the competitive dynamics and accelerating the adoption of intelligent HR technologies across the global workforce. Further analysis of early customer experiences will be crucial in understanding the long-term implications of this strategic move.
