This week marked a significant development in the rapidly evolving landscape of Human Resources technology with Gloat, a recognized leader in skills intelligence and talent marketplaces, unveiling its ambitious foray into AI Agents for HR. This strategic move underscores the escalating competition for dominance in core HR technology, as companies increasingly seek to leverage artificial intelligence to streamline and enhance talent management processes. Gloat’s new offering, Gloat Agentic HR, aims to empower organizations to rapidly develop AI agents that integrate seamlessly with popular communication and AI platforms like Microsoft Copilot, Teams, and Slack. Crucially, these agents are designed to operate within the established business rules and security frameworks of existing enterprise systems such as Oracle, Workday, and SuccessFactors.
The Evolving Architecture of Agentic AI in HR
Understanding Gloat’s strategic positioning requires an examination of the emerging architectural layers of AI agents within enterprise environments. At the foundational level lie the "systems of record"—the robust Human Capital Management (HCM) and Enterprise Resource Planning (ERP) applications that store, maintain, and update critical organizational data. These include platforms like Workday, SAP, Oracle, and UKG, which manage everything from employee demographics and payroll to financials and inventory.
Layered above these core systems are applications that bridge multiple systems. In large enterprises, a complex ecosystem of hundreds of specialized applications—including time-tracking systems, Learning Management Systems (LMS), Applicant Tracking Systems (ATS), and IT provisioning tools—interconnect to deliver specific functionalities. Many of these applications directly impact employee experience. Over the past two decades, as cloud adoption has surged, companies have built intricate solution architectures. ServiceNow, a prominent player in this space, has established a dominant position with its focus on workflow automation and service management for global organizations. Simultaneously, employee experience platforms such as Microsoft Viva, Zoom’s Workvivo, Firstup, and Staffbase, alongside tools built on Google and Slack, have gained traction. However, many of these platforms were not initially designed with "agentic" capabilities in mind.
The third layer represents the new generation of AI Agents. These agents transcend mere portals or workflow aggregators; they possess intelligence about individual users and can perform actions on their behalf. The HR technology sector is now witnessing an influx of agent tools, ranging from those developed by nimble startups to capabilities embedded within existing enterprise systems, all aimed at facilitating the creation of cross-functional HR solutions.
Capping this architectural pyramid are "Superagents." These advanced tools orchestrate and access multiple functional agents, providing users with an exceptionally intuitive, "walk-up-and-use" experience. This layered architecture is currently experiencing rapid expansion, with numerous tools entering the market. Notable examples include Sana (integrated with Workday), Oracle Agent Studio, Microsoft Copilot Studio, Leena.ai, and ServiceNow (which has been bolstering its agent capabilities through acquisitions, such as MoveWorks). SAP is also contributing with its Joule Studio, alongside foundational AI providers like Anthropic, OpenAI, and Google.
Operationalizing AI Agents: The Challenge of Business Rules and Context
While the potential of agentic AI is undeniable, its practical implementation is fraught with complexities, particularly concerning the integration of business rules, security protocols, and legacy systems. For instance, a payroll reconciliation agent, whether from Workday, SAP, or Oracle, needs to be meticulously "trained" with a company’s specific rules and security policies to effectively monitor payroll transactions, reconcile tax data, and account for employee movements, terminations, new hires, and pay adjustments. Furthermore, to address sophisticated use cases like pay equity monitoring or performance-based adjustments, these agents must seamlessly communicate with other specialized agents.
This interconnectedness highlights 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 AI agent designed for this purpose would need to assess job fit, identify skill gaps, and evaluate redeployment potential before any significant layoffs or hiring initiatives. Such an agent would require access to a multitude of other agents to function effectively.
In a real-world application, such as the one developed by Galileo, an HR professional agent, the system must comprehend job and skill requirements. It then guides employees and managers through their available options, suggesting relevant training programs, factoring in geographical constraints, licensing requirements, union agreements, and pay scales. The complexity of such "Superagents" underscores the intricate nature of effective AI deployment in HR.
Beyond talent redeployment, common use cases for agentic AI in HR span global onboarding, promotion processes, annual reviews and compensation cycles, talent acquisition, employee certification management, and time and schedule optimization.
The Battle for Enterprise Agents: Securing and Leveraging Business Rules
Every major HR technology vendor is actively developing agent capabilities. These range from simple coaching tools to more advanced functionalities. Leading vendors like Workday, Oracle, SAP, and ServiceNow are investing heavily in AI studios, aiming to simplify agent development for their client base.
However, the core challenge transcends simply acquiring or building individual agents. The true battle lies in the ability to seamlessly integrate these agents and, critically, to manage the underlying business rules, security policies, existing workflows, and unique business "objects" that define an organization’s operational model. These business objects, such as a company’s defined career framework or its performance review methodology, represent the core intellectual property and competitive advantage of a business. As organizations evolve, these rules and objects must adapt accordingly. The objective is to avoid hardcoding these dynamic elements directly into agents, instead enabling agents to access a "semantic layer" that accurately reflects and maintains this critical information.
Established HCM vendors, including Workday, Oracle, SAP, and UKG, possess inherent semantic layers, such as Workday’s Business Process Framework. They are now integrating these foundational elements into their agent development tools. Consequently, any HR agent or agent development platform will seek to leverage this crucial layer. This integration strategy might even challenge certain predictions of a "SaaSpocalypse," suggesting a continued emphasis on integrated, platform-centric solutions.
Gloat’s Strategic Entry: Loomra and Agentic HR
Gloat’s recent launch positions itself as a liberator from the constraints of legacy approaches. The company has developed an "agent-driven auto-discovery ‘injector’" that meticulously mines entities, workflows, and business rules from existing HCM systems, ensuring continuous synchronization as these core systems evolve. This technology, branded as Loomra, effectively replicates the rules and objects embedded within an organization’s HCM, thereby simplifying the process of building custom applications on top of these foundations.
Gloat further offers an intuitive agent builder, enabling users to visually construct their own agents. These custom agents can then be deployed directly into widely used platforms such as Microsoft Teams, Copilot, and Slack. The company has already developed pre-built agents addressing critical HR functions, including Workforce Redeployment, Career Development, Internal Talent Sourcing, Succession Planning, and Learning & Reskilling—areas where Gloat possesses significant existing expertise.
Workforce Context as a Differentiator
The key differentiator of Gloat’s Agentic HR platform lies in its robust workforce context layer. Loomra, Gloat’s Workforce Context Engine, is built upon nearly a decade of enterprise-scale workforce data analysis. It models skill adjacencies, career trajectories, organizational patterns, and workforce relationships across millions of employees globally.
This deep contextual understanding enables 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 proactively identify employees suitable for 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 Gloat’s Position
Gloat’s approach offers a compelling proposition: organizations can potentially bypass the lengthy development cycles of their legacy HCM vendors by leveraging Gloat’s platform to build upon their existing infrastructure. The open nature of Gloat’s platform also facilitates integration with other internal systems. Furthermore, Gloat’s context engine appears to be a unique offering, with no immediate indication that other major HCM vendors have developed a comparable system.
However, the market for agent tools is intensely competitive. Organizations heavily invested in ServiceNow will likely continue to utilize its platform. Workday customers are expected to gravitate towards Sana, while Oracle users will opt for Oracle AI Studio, and SAP clients will leverage Joule Studio. As Workday articulated in a recent blog post, their agent strategy is deeply integrated with their core platform.
Gloat’s challenge will be to convincingly demonstrate that its tools offer superior ease of use, seamless integration with existing HCM systems, and more profound context-awareness than competing solutions. As the enterprise shifts from "systems of record" to "systems of context," Gloat has the potential to emerge as a leader.
Future Outlook and Market Dynamics
The evolution of application development tools has historically followed patterns where companies adopt toolsets that align with their existing technological infrastructure. Gloat’s new platform represents an innovative step forward, pushing the boundaries of the agent industry.
As early customer adoption unfolds, insights into the practical application and impact of Gloat’s Agentic HR platform will become clearer. The company’s ability to navigate the competitive landscape by offering a compelling blend of ease of use, deep integration, and advanced contextual intelligence will be critical to its long-term success in redefining how organizations manage their talent in the age of AI.
Additional Information
- Workday and Sana Unveil A Bold New Strategy For AI: [Link to relevant article]
- Agents, Superagents, and Intelligent Orchestration: 2026 Imperatives for Enterprise AI: [Link to relevant article]
- The L&D Revolution Has Arrived: AI Enables Dynamic Enablement For All: [Link to relevant article]
- Get Galileo, the AI Superagent for HR: [Link to relevant article]
