The landscape of human resources technology is undergoing a significant transformation with the emergence of AI agents, a field that is rapidly becoming a battleground for innovation and market share. Gloat, a recognized pioneer in skills intelligence and talent marketplace solutions, has made a bold move into this burgeoning domain, launching its AI Agentic HR platform. This strategic entry underscores the intensifying competition for dominance in core HR technology and highlights the evolving expectations of how employees will interact with HR systems.
Gloat’s offering, branded as Gloat Agentic HR, is designed to harness the power of artificial intelligence while seamlessly integrating with existing enterprise systems. The platform provides a robust toolset that leverages the established business rules and security protocols inherent in major HR platforms such as Oracle, Workday, and SuccessFactors. This allows organizations to rapidly develop and deploy AI agents that can operate within popular communication and collaboration environments, including Microsoft Copilot, Teams, Slack, and other emerging AI applications. The implications of this development are far-reaching, suggesting a future where HR functions are more proactive, personalized, and accessible than ever before.
The Evolving Architecture of AI Agents in HR
To fully grasp the significance of Gloat’s announcement, it’s crucial to understand the layered architecture that defines the current AI agent landscape. This framework helps contextualize how new technologies like Gloat’s fit into the broader ecosystem of enterprise software.
Layer 1: Systems of Record (Core HCM and ERP)
At the foundational level lie the systems of record, the digital backbone that stores, updates, and maintains an organization’s critical data. These include Human Capital Management (HCM) applications like Workday, SAP, Oracle, and UKG, which sit atop complex databases managing financial, customer, employee, inventory, and product information. These systems are the authoritative sources of truth for an organization’s operational and personnel data, built over decades to ensure data integrity and compliance.
Layer 2: Cross-System Applications and Employee Experience Platforms
Overlaying the systems of record is a layer of applications designed to bridge the gaps between disparate systems and enhance the employee experience. Given that no single vendor offers a complete solution, organizations typically build custom portals, mobile applications, and workflows that connect these core systems with hundreds of specialized applications. These can range from time-tracking systems and Learning Management Systems (LMS) to Applicant Tracking Systems (ATS) and IT provisioning tools. The average large enterprise, for instance, manages over 400 such applications, with more than 100 directly impacting employee interactions.
Over the past two decades, as businesses migrated from on-premises infrastructure to cloud-based solutions, a rich ecosystem of specialized applications has flourished. Companies like ServiceNow, a titan in the IT service management and workflow automation space with a market capitalization exceeding $13.5 billion and a consistent annual growth rate of approximately 20%, have become dominant players in this layer for global organizations. Furthermore, a wave of employee experience platforms, including Microsoft Viva, Zoom’s Workvivo, Firstup, and Staffbase, along with tools built on Google and Slack, are increasingly deployed to improve employee engagement and communication. However, many of these platforms were not originally designed with "agentic" capabilities in mind, meaning they lack the inherent intelligence and autonomy to act on behalf of users.
Layer 3: AI Agents – The New Frontier
The third layer represents the emerging category of AI agents. These are not mere portals or static workflows; they are intelligent entities capable of understanding user context, learning individual preferences, and proactively performing tasks on behalf of employees. The HR technology sector is witnessing an explosion of agent tools, ranging from offerings by agile startups to those embedded within larger enterprise systems. These agents aim to facilitate the creation of cross-functional HR solutions that are more dynamic and responsive to individual needs.
Layer 4: "Superagents" – Orchestrating the Agent Ecosystem
Crowning this architecture are what are termed "Superagents." These advanced tools are designed to stitch together and orchestrate multiple functional agents, creating a more unified and intuitive user experience. They aim to provide an "out-of-the-box" or "walk-up-and-use" interaction model, abstracting away the underlying complexity of individual agents and systems for the end-user. This layer represents the ultimate goal of seamless, intelligent automation in the workplace.
The current market for agent development tools is vibrant and competitive. Notable players include Sana (integrated with Workday), Oracle Agent Studio, Microsoft Copilot Studio, Leena.ai, and ServiceNow (which has significantly bolstered its agent capabilities through its acquisition of MoveWorks). SAP is also advancing its AI strategy with Joule Studio, while foundational AI providers like Anthropic, OpenAI, and Google are enabling the development of a wide array of agents. Tools like Galileo, described as an AI-powered advisor or HR professional agent, exemplify the potential for these agents to democratize access to information and HR services for employees.
The Mechanics of Agentic HR: Navigating Complexity
While the concept of AI agents in HR is undeniably exciting, their practical implementation is fraught with complexities. The success of any AI agent hinges on its ability to accurately interpret and act upon an organization’s intricate web of business rules, security protocols, and legacy system constraints.
Consider, for example, a payroll reconciliation agent. Such an agent, if offered by a major vendor like Workday, SAP, or Oracle, would typically monitor payroll transactions, reconcile tax information, time-card data, employee status changes (moves, terminations, new hires), and pay adjustments. However, its effectiveness is entirely dependent on its ability to be "trained" with the specific rules and security policies of the deploying company. Furthermore, if the agent is tasked with monitoring pay equity or factoring in performance-based adjustments, it must be able to communicate and collaborate with other agents or systems that hold relevant data.
This highlights the critical importance of "agent architecture." Imagine a scenario where an organization needs to redeploy 5,000 employees from existing roles to new positions. An AI agent tasked with this initiative would need to analyze factors such as job fit, identify skills gaps, and assess potential for internal movement before initiating large-scale layoffs or recruitment drives. Such a sophisticated agent would require seamless integration with a multitude of other agents and systems, including those managing skills inventories, job requirements, learning and development resources, and organizational structure.
In a hypothetical example, an agent built for workforce redeployment would need to understand job requirements and employee skill sets to coach both employees and managers on available options. It would also need to be aware of training programs, location constraints, licensing requirements, union agreements, and compensation bands. The complexity of orchestrating these various data points and decision-making processes underscores the sophisticated architecture required for truly effective agentic solutions.
Common use cases that are being targeted by AI agents include global onboarding processes, career development and internal mobility, annual performance reviews and compensation cycles, talent acquisition, employee certification management, and intricate time and schedule management. Each of these areas presents unique challenges in data integration and rule interpretation.
The Battle for Enterprise Agents: The Crucial Role of Business Rules
The proliferation of AI agents in the HR technology market has sparked a fierce competition among vendors. While many are offering straightforward coaching tools, others are developing more advanced capabilities. The major players—Workday, Oracle, SAP, and ServiceNow—are investing heavily in "AI studios" to empower organizations to build and customize their own agents more easily.
However, the central challenge is not merely about acquiring or developing individual agents. The true battle lies in the ability to seamlessly integrate these agents into a cohesive ecosystem. This integration must respect and leverage existing business rules, security frameworks, established workflows, and proprietary "business objects" that define an organization’s unique operational model. A "business object" could encompass anything from a company’s specific career framework to its performance review methodology.
These rules and rubrics are not static; they represent the core value proposition and operational logic of a business, evolving as the company itself changes. The goal is to avoid hard-coding these dynamic rules directly into individual agents. Instead, agents need to access a "semantic layer" that centrally manages and interprets this crucial business information.
The incumbent HCM vendors, including Workday, Oracle, SAP, and UKG, already possess sophisticated semantic layers, such as Workday’s Business Process Framework. They are actively integrating these foundational components into their agent development tools, recognizing their strategic importance. Consequently, any HR agent or agent development platform will need to tap into this vital layer to ensure accurate and contextually relevant operations. This dynamic may challenge some predictions of a "SaaSpocalypse," suggesting that established enterprise systems will play a crucial role in the AI-driven future.
Gloat’s Strategic Play: Loomra and Agentic HR
Gloat aims to liberate organizations from the constraints of traditional, siloed approaches to HR technology with its new Agentic HR platform. 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 critical entities, workflows, and business rules. Crucially, Loomra stays synchronized with the HCM system, ensuring that the replicated rules and objects remain up-to-date as the core system evolves. This effectively creates a dynamic semantic layer that can be leveraged by external applications.
Gloat then offers an intuitive agent builder, allowing users to visually construct their own AI agents. These agents can be directly integrated into popular platforms like Microsoft Teams, Copilot, and Slack, enabling seamless deployment and interaction. Gloat has already developed pre-built agents for key HR functions where it possesses significant expertise, including Workforce Redeployment, Career Development, Internal Talent Sourcing, Succession Planning, and Learning & Reskilling.
Workforce Context as the Differentiating Factor
What sets Gloat’s Agentic HR platform apart is the robust workforce context layer that underpins it. Gloat’s Workforce Context Engine, Loomra, 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 deep 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 perform sophisticated reasoning. For instance, 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, or recommend optimal talent redeployment strategies in response to shifting business priorities. This proactive and predictive capability represents a significant leap forward in HR intelligence.
A Competitive Landscape and Gloat’s Position
Gloat’s offering presents a compelling proposition: organizations can potentially bypass the lengthy development cycles of their legacy HR vendors and begin building sophisticated AI-driven HR solutions immediately. The open nature of the Gloat platform allows for integration with other internal systems, fostering a more connected enterprise. Moreover, Gloat’s unique context engine, built on extensive workforce data, is something that many other HCM vendors have yet to develop at a comparable scale.
However, the market for AI agent tools is exceptionally competitive. Organizations already heavily invested in specific HCM ecosystems are likely to leverage the agent development tools provided by their primary vendors. Workday customers, for example, will likely gravitate towards Sana, while Oracle and SAP users will turn to their respective Oracle AI Studio and Joule Studio. ServiceNow customers, particularly those who have embraced its platform, will utilize its enhanced agent capabilities.
Gloat’s challenge is to persuade potential customers that its tools are not only easier to use and fully integrated but also offer superior contextual intelligence compared to alternatives. As the enterprise technology landscape shifts from "systems of record" to "systems of context," Gloat has the potential to emerge as a leader, provided it can effectively demonstrate the value of its unique approach.
The Future of Enterprise AI and Application Development
The evolution of application development tools has historically followed a pattern where new platforms emerge to address emerging needs. From early visual development tools to modern AI-powered coding assistants, companies tend to adopt solutions that align with their existing infrastructure and strategic priorities. Gloat’s innovative platform represents a significant advancement in the agent industry, pushing the boundaries of what’s possible in HR automation and intelligence.
The coming months will be critical as Gloat rolls out its Agentic HR platform and engages with early customers. Observing how these organizations leverage Loomra and the agent builder to address complex HR challenges will provide valuable insights into the future of AI in the enterprise. The ability of these new AI agents to effectively integrate with legacy systems, interpret nuanced business rules, and deliver actionable intelligence will determine their ultimate impact on how businesses manage their most valuable asset: their people.
The ongoing dialogue and development in this space, including initiatives like Workday’s strategic advancements in agentic AI and the broader exploration of "Superagents" and intelligent orchestration, signal a fundamental shift. This shift promises to redefine employee enablement and create more dynamic, personalized, and efficient HR functions for the future.
