Corporate networks are filling up with AI agents, creating a governance blind spot for leaders managing multi-cloud infrastructures.
As distinct business units race to adopt generative technologies, CIOs especially find their ecosystems populated by fragmented and unmonitored assets. This mirrors the shadow IT challenges of the cloud era, but involves autonomous actors capable of executing business logic and accessing sensitive data.
IDC projects the number of actively deployed AI agents will exceed one billion by 2029—a forty-fold increase from current levels. In the first half of 2025 alone, agent creation surged by 119 percent. For enterprise leadership, the immediate challenge shifts from building these agents to locating, auditing, and governing them across platforms.
Salesforce has responded to this fragmentation by expanding its MuleSoft Agent Fabric capabilities, introducing automated discovery tools designed to centralise the management of AI agents regardless of their origin.
Automating discovery
Visibility remains the core issue for security and operations teams. When marketing teams deploy AI agents on one platform and logistics teams build on another, effective governance becomes difficult as central IT loses a consolidated view of the organisation’s digital workforce.
MuleSoft’s updated architecture addresses this via ‘Agent Scanners’. These tools continuously patrol major ecosystems – including Salesforce Agentforce, Amazon Bedrock, and Google Vertex AI – to identify running agents. Rather than relying on developers to manually register their deployments, the system automates detection.
Finding an agent is only the first step; compliance leaders need to understand the logic behind it. The scanners extract metadata detailing the agent’s capabilities, the LLMs driving it, and the specific data endpoints it is authorised to access. This information is then normalised into standard Agent-to-Agent (A2A) specifications, creating a uniform profile for assets regardless of the underlying vendor.
Andrew Comstock, SVP and GM of MuleSoft, said: “The most successful organisations of the next decade will be those that harness the full diversity of the multi-cloud AI landscape. The expanded capabilities of MuleSoft Agent Fabric give you the freedom to innovate across any platform while maintaining the unified visibility and control needed to scale.”
Governance and cost control for AI agents
Unmanaged agents create financial inefficiency and risk exposure. Consider a CISO in the banking sector. Under standard operations, verifying a new loan-processing agent involves manually chasing documentation from development teams. Automated cataloguing allows security teams to immediately view which financial databases an agent accesses and verify its authorisation levels without manual intervention. This capability ensures security teams view real-time data rather than outdated snapshots.
From a financial perspective, visibility drives consolidation. Large enterprises frequently suffer from redundancy where regional teams independently procure or build similar tools. A multinational manufacturer, for instance, might have three separate teams paying for distinct summarisation agents on different platforms.
By using the MuleSoft Agent Visualizer to filter the estate by job type, operations leaders can identify these overlaps. Consolidating these into a single high-performing asset reduces redundant licensing costs and allows budget reallocation toward novel development.
Transitioning successfully to an ‘Agentic Enterprise’
Innovation often occurs at the edges, where data scientists build bespoke tools outside formal procurement channels.
The expanded Agent Fabric addresses this by allowing the registration of “homegrown” agents and Model Context Protocol (MCP) servers via URL. This is particularly relevant for sectors like logistics, where teams may build internal tools for proprietary database optimisation. Instead of remaining hidden, these assets can be registered and made discoverable for reuse across the company.
Jonathan Harvey, Head of AI Operations at Capita, said: “Agent Scanners will let us focus on innovation instead of inventory management. Knowing that every agent is automatically discovered and catalogued allows our teams to collaborate, reuse work, and build smarter multi-agent solutions.”
Similarly, AT&T is utilising the framework to orchestrate agents across customer support, chat, and voice interactions.
Brad Ringer, Enterprise & Integration Architect at AT&T, explained: “With AI moving so fast, MuleSoft Agent Fabric provides the framework we need to scale. It brings together and helps us orchestrate all of the agents and MCP servers we’re building in customer support, chat, and voice interactions. It isn’t just a tool; it’s a huge enabler for everything we’re doing next.”
The transition to an “Agentic Enterprise” requires a change in governance around how IT assets are tracked, rendering the days of managing integrations via stale spreadsheets incompatible with the speed of AI agent deployment.
Leaders must assume their inventory of AI agents is incomplete and deploy automated scanning tools to establish a baseline of truth. Once this baseline is established, governance policies should mandate that all agents – whether bought or built – expose their capabilities and data access privileges in a standardised format like A2A to facilitate monitoring.
Finally, executives can use the visibility provided by these tools to audit spend, identifying duplicate functionalities across cloud environments and merging them to control the Total Cost of Ownership (TCO).
As organisations move from pilot programmes to mass deployment, the differentiator will not be the intelligence of individual agents, but the coherence of the network that connects them.
See also: Balancing AI cost efficiency with data sovereignty
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