Corporate networks are quietly filling up with AI agents, creating a growing governance blind spot for technology leaders operating across complex, multi-cloud environments.
As business units rapidly adopt generative AI tools, CIOs are discovering ecosystems crowded with fragmented, poorly monitored agents. The situation echoes the shadow IT problem of the early cloud era — but with a critical difference. These new assets are autonomous, capable of executing business logic, and often have access to sensitive data.
From experimentation to oversight risk
The scale of agent proliferation is accelerating fast. Industry forecasts suggest the number of actively deployed AI agents will surpass one billion within the next few years, representing a dramatic increase from today’s levels. Agent creation is no longer confined to central IT teams; it is happening across marketing, operations, finance, and customer service.
As a result, the leadership challenge has shifted. Building agents is no longer the hard part. Locating them, understanding what they do, and ensuring they operate within governance boundaries has become the real test.
Visibility is the missing foundation
The primary obstacle facing security and operations teams is visibility. When different departments deploy agents across platforms like Salesforce, Amazon Bedrock, or Google Vertex AI, central IT quickly loses a unified view of the organisation’s digital workforce.
Salesforce’s expanded MuleSoft Agent Fabric addresses this issue through automated discovery. Its “Agent Scanners” continuously search major ecosystems to identify active agents, removing the reliance on manual registration by development teams.
Once discovered, agents are profiled through extracted metadata that details their capabilities, underlying language models, and authorised data access points. This information is normalised into Agent-to-Agent (A2A) specifications, creating a consistent governance layer regardless of vendor or platform.
Governance goes beyond security
Understanding what agents exist is only the first step. Governance also has financial implications.
Unmanaged agents often introduce hidden inefficiencies. Large enterprises frequently end up paying multiple times for similar capabilities built independently by regional or functional teams. Without a consolidated view, redundancy becomes invisible.
Tools like MuleSoft’s Agent Visualizer allow leaders to group agents by function and identify overlaps. Consolidating similar agents into a single, high-performing solution reduces licensing costs and frees up budget for innovation rather than duplication.
Supporting innovation without losing control
AI innovation often happens at the edges of the organisation. Data scientists and engineers build bespoke agents outside formal procurement processes, particularly in industries with proprietary data and workflows.
Modern governance frameworks must accommodate this reality. MuleSoft’s expanded Agent Fabric allows internally developed agents and Model Context Protocol (MCP) servers to be registered and made discoverable. Instead of being hidden risks, these assets become reusable components across the enterprise.
This approach enables organisations to balance decentralised innovation with central oversight — a requirement for scaling agent-based systems responsibly.
Moving toward the agentic enterprise
The shift to what many describe as the “Agentic Enterprise” requires a rethink of how IT assets are tracked and governed. Static inventories and spreadsheets are fundamentally incompatible with the speed at which AI agents are deployed.
Leaders should assume their current agent inventory is incomplete. Automated discovery establishes a baseline of truth, while governance policies must require all agents — whether purchased or internally built — to expose their capabilities and data permissions in a standardised format.
With visibility in place, executives gain the ability to audit spend, reduce duplication, and manage total cost of ownership across cloud environments.
As AI moves from pilots to large-scale deployment, competitive advantage will not come from having the smartest individual agents. It will come from having a coherent, governed network that connects them.
