In a significant move to address the escalating complexity of artificial intelligence deployments within large organizations, Salesforce has introduced Agent Scanners, a new feature for its MuleSoft integration platform designed to manage the growing challenge of “agent sprawl.” As a key component of the broader Agent Fabric suite launched last year, this automated discovery tool provides a foundational solution to the visibility, governance, and efficiency problems created by the rapid, often uncoordinated, adoption of AI agents across the enterprise. The system is engineered to bring order to the chaos, offering a centralized view of a landscape that has become increasingly fragmented and difficult to control, thereby enabling businesses to scale their AI initiatives in a secure and strategic manner.
Confronting the Chaos of AI Proliferation
The Problem of Agent Sprawl and Shadow AI
The pervasive issue of agent sprawl emerges as different teams and departments within an enterprise independently adopt various agentic products and build custom AI solutions, leading to a chaotic and fragmented technological landscape. This fragmentation results in a proliferation of agents with redundant workflows, siloed operations, and a stark lack of interoperability, which directly undermines operational efficiency. The consequences of this uncontrolled growth are significant, creating duplicated efforts and wasted resources while severely complicating governance. For Chief Information Officers and security leaders, this environment makes it nearly impossible to scale artificial intelligence initiatives in a safe, responsible, and cost-effective manner, as they lack a comprehensive understanding of what AI tools are active within their own infrastructure and what data those tools are accessing. This challenge is not merely technical but represents a fundamental barrier to achieving a cohesive and strategic enterprise AI posture.
The proliferation of unmonitored AI tools has also given rise to the phenomenon of “shadow AI,” where agents are developed and deployed without the knowledge or oversight of central IT and security teams. These shadow agents, often built within disparate cloud silos to solve immediate, team-specific problems, introduce substantial security vulnerabilities and compliance risks. Because they operate outside of established governance frameworks, they can expose sensitive corporate data and create unforeseen attack vectors. Furthermore, their isolated nature prevents them from being integrated into broader, more powerful workflows, thereby limiting their potential value and contributing to a growing technical debt. The lack of a unified discovery mechanism means these agents remain hidden, making any attempt at comprehensive risk assessment or strategic planning an exercise in futility. This hidden layer of technology deepens the fragmentation, making it exceedingly difficult to build a truly interconnected and intelligent enterprise.
The Scanner as a Centralized Solution
To combat this critical visibility gap, Agent Scanners provides a direct and essential solution engineered to automatically discover and catalog AI agents across a diverse range of environments and platforms. The tool is designed to identify agents operating on prominent systems such as Microsoft’s Copilot, Google’s Vertex AI, Amazon’s Bedrock, and Salesforce’s own Agentforce. Once an agent is detected, the scanners meticulously synchronize both the agent and its associated metadata with the Agent Registry. This registry, another integral tool within the Agent Fabric suite, functions as a centralized, authoritative directory for all AI components within the organization. By automatically populating this registry, Agent Scanners makes these agents—whether developed in-house or sourced from third-party vendors—discoverable to other agents and developers, which is the first step toward fostering a more connected, efficient, and reusable AI ecosystem across the entire enterprise.
The Agent Registry serves as the foundational element for a comprehensive management framework, acting as the single source of truth for an organization’s AI assets. By cataloging the capabilities, metadata, and endpoints of all identified agents, it transforms a previously chaotic collection of disparate tools into an organized, searchable inventory. This centralized view is complemented by other tools within the Agent Fabric suite, including Agent Broker, Agent Visualizer, and Agent Governance, which together provide a holistic approach to AI management. The Agent Broker facilitates secure communication between agents, the Visualizer offers a graphical representation of the agent landscape and their interactions, and the Governance component allows for the enforcement of policies and access controls. This comprehensive suite moves beyond simple discovery to provide the necessary tools for active management, security, and strategic scaling of AI initiatives.
The Power of Automated Discovery and Governance
The Critical Need for Automation
The seemingly simple function of automated discovery is, in fact, a foundational capability for modern enterprises, a point underscored by industry experts. Robert Kramer, a principal analyst at Moor Insights and Strategy, emphasizes that the most pressing issue for CIOs today is not the deployment of new agents but achieving a clear understanding of the existing landscape. Many organizations lack answers to basic questions, such as the total number of agents in operation, their deployment locations, the large language models they utilize, and the specific data they are authorized to access. Agent Scanners is designed to directly address this fundamental lack of visibility. By providing a clear and current inventory, it allows leadership to identify and reduce fragmentation caused by teams building similar agents in isolation, thereby preventing duplicated effort, promoting internal reuse of existing assets, and establishing a baseline for effective governance and strategic planning.
The critical importance of the feature’s automated nature cannot be overstated, as manual tracking methods for AI agents are already proving to be unsustainable and are rapidly becoming obsolete. According to Stephanie Walter, a practice leader at HyperFRAME Research, any manually maintained registry would quickly devolve into a “stale spreadsheet,” failing to account for the numerous shadow AI agents being built within various cloud silos. Given the accelerating pace of agent creation, a static, manual approach is incapable of keeping up with the dynamic reality of enterprise AI. Walter posits that an automated scanner is essential not just for documenting agent sprawl but for actively curbing it, aligning with the original strategic goal of the Agent Fabric initiative. Without such automation, any attempt at creating a comprehensive and accurate agent registry is destined for failure, leaving the organization vulnerable to the risks associated with unmanaged AI.
From Simple Inventory to Intelligent Governance
The overarching trend this technology supports is the shift from simple inventory management to intelligent, context-aware governance. The real value of Agent Scanners lies in its ability to extract and document rich metadata beyond the mere existence of an agent. As Kramer notes, knowing an agent is present is only the first step; the crucial insights come from understanding its context—what it is capable of, which LLM powers it, and what data it can touch. This detailed metadata empowers various stakeholders across the organization. For instance, security teams can more effectively assess and mitigate the risks associated with each agent’s permissions and data access. Similarly, solution architects can analyze the collected data to identify opportunities for consolidation, eliminate functional overlap between agents, and design more efficient enterprise-wide systems. This transforms agent management from a passive tracking exercise into a dynamic process.
This enriched, contextual information allows developers to safely and confidently connect agents, building more complex and powerful workflows that drive tangible business value. The detailed metadata can be standardized into formats like Agent-to-Agent (A2A) cards, which would further streamline the process of identifying, understanding, and trusting agents across the enterprise. Such a standard would function like a digital passport for each agent, clearly stating its identity, purpose, and capabilities. This approach transforms agent management from a passive, static tracking exercise into a dynamic process that supports real-time operational decisions, cost optimization, and strategic planning. By providing this deeper layer of intelligence, the system enables a more sophisticated and secure approach to scaling AI, ensuring that new deployments are additive rather than duplicative and that the entire ecosystem operates in a cohesive, governed manner.
A New Foundation for Enterprise AI
MuleSoft’s Agent Scanners feature represented a crucial infrastructural development for enterprises navigating the complexities of modern AI. It directly confronted the chaos of agent sprawl by providing automated, continuous discovery and cataloging, which offered CIOs and security leaders the long-missing visibility required for effective governance, risk management, and operational efficiency. By automating the population of a central Agent Registry with rich, contextual metadata, it laid the groundwork for a more cohesive, secure, and scalable enterprise AI strategy. This innovation moved the industry beyond mere documentation to enable intelligent, data-driven decisions, transforming how organizations managed and leveraged their growing fleets of AI agents. The tool, initially available in a preview release, reached its general availability by the end of its launch month, marking a new chapter in enterprise AI management.
