For years, enterprises have focused on digital transformation – connecting systems, building APIs, and delivering experiences that span multiple applications. This era of API-led connectivity solved a big problem: how to manage sprawling systems and scattered data while helping teams move faster. But now, a new wave of innovation is emerging. This is the shift from simply connecting systems to empowering them to act on their own.
Enter the agentic enterprise: an ecosystem where intelligent AI agents can perceive what’s happening across your business, make decisions, and collaborate in real time. These aren’t simple chatbots or one-off automation scripts. They’re capable of complex, multi-step workflows and can adapt as situations evolve. They enable an enterprise where human and agent labor work together to drive key business processes.
AI agents have enormous potential. They promise major productivity gains, faster innovation cycles, and more responsive business operations. This explosion of creativity is a powerful force but comes with new challenges and risks.
The challenges of a fragmented agent ecosystem
As AI agents multiply across SaaS platforms, custom solutions, and hyperscaler ecosystems, organizations can quickly find themselves dealing with agent sprawl, a kind of fragmented complexity that starts to chip away at the value these agents were meant to deliver.
Picture this: each department, team, or SaaS tool running its own intelligent agents with little coordination. Before long, you have hundreds of autonomous actors operating independently. Each one might have its own priorities, data access rules, and methods for interacting with systems.
Without a central way to govern, orchestrate, or monitor them, workflows that should be seamless start to break down. One agent might duplicate another’s work. Two might update the same system with conflicting information. Or key steps in a process could be skipped entirely because no agent “owns” that part of the workflow.
Over time, this fragmentation creates inconsistent processes, more errors, and unnecessary delays. Teams lose visibility into what’s actually happening, making troubleshooting and auditing a challenge. Operational risk grows as sensitive data flows through multiple agents without consistent governance or security oversight.
Even simple tasks like onboarding a new employee or processing an order can turn into fragile, error-prone workflows when handled by a loosely connected web of autonomous agents. The more they proliferate without coordination, the more unpredictable the system becomes, putting both efficiency and compliance at risk.
These fragmented workflows and hidden risks are all symptoms of agent sprawl. Even without building a single custom agent organizations will inherit thousands of agents embedded in SaaS applications making the governance of this unmanaged workforce an unavoidable imperative.
If these challenges sound familiar, it’s because enterprises faced similar issues during the API boom. Back then, IT teams struggled to keep pace with developers spinning up new APIs and integrations, creating silos and operational friction. MuleSoft provided a unified, flexible foundation for scaling digital transformation through API-led connectivity. Now, with MuleSoft Agent Fabric, we’re doing it again with AI agents.
How MuleSoft Agent Fabric helps organize the agentic enterprise
Designed to help organizations harness the power of autonomous agents without losing control, MuleSoft Agent Fabric (MAF) is a unified foundation where agents and their associated tools can be discovered, orchestrated, governed, and observed. Think of it as a central nervous system for your AI ecosystem – an intelligent, governable layer that connects, coordinates, and monitors every agent in your enterprise.
By offering a unified control plane and agent management platform, MAF mitigates the risks of agent sprawl while allowing teams to experiment, iterate, and deploy autonomous capabilities confidently. Agent Fabric leverages proven MuleSoft Anypoint capabilities to provide a unified platform for agentic and digital transformations.
Some of the core components of MuleSoft Agent Fabric include:
- Agent Registry: A centralized registry makes every agent, MCP server and tool in the organization easily discoverable. Teams can reuse existing assets instead of reinventing the wheel.
- Agent Broker: An intelligent agent broker routes tasks to the best-suited agent or tool, automating complex workflows and ensuring business goals are achieved efficiently.
- Agent Governance: Flex Gateway extends proven enterprise-grade security, control and policy enforcement to autonomous agents, ensuring compliance, access control, and data protection.
- Agent Visualizer: A real-time visualizer provides transparency into agent interactions, performance, and dependencies, helping teams understand and optimize their agent ecosystem.
By unifying these capabilities, MAF helps organizations transform scattered networks of agents into composable, high-performing ecosystems that are both agile and secure.
How organizations can start building an agent network
One of the biggest advantages of MuleSoft Agent Fabric is its design for incremental adoption. You don’t have to rebuild everything from scratch or commit to a massive overhaul on day one. You can start small, see results quickly, and add capabilities as your strategy matures.
A typical journey to build a network of agents might unfold like this:
- Register what you already have: Catalog your existing agents, MCP assets, APIs, and tools in the centralized Agent Registry so they’re easy to find and manage
- Configure the agent broker: Set your business goals and define how registered agents and tools should interact
- Apply governance policies: Use Flex Gateway to enforce consistent security, compliance, and access controls across every agent and API interaction
- Visualize and monitor: Deploy the agent broker and track your workflows in real time through the Agent Visualizer
You can also adopt capabilities incrementally:
- Making APIs LLM-callable: Wrap existing APIs with MCP so LLMs can intelligently interact with them
- Standardizing agents across teams: Use A2A protocols to ensure agents follow consistent patterns and can seamlessly collaborate
- Curate agents and tools: Catalog and curate officially approved set of agents and MCP tools
- Govern agents and tools: Apply out-of-box A2A and MCP policies (including rate limiting) to secure and control agent interactions
- Coordinating multi-agent workflows: Orchestrate complex processes where multiple agents interact dynamically, handing off tasks and updating systems without human intervention
- Visualize agent interactions: get a visual map of agent interactions, trace agent behavior and analyze impact of individual components
These steps help organizations scale their agentic capabilities in a controlled, governed way – turning individual experiments into a fully orchestrated, enterprise-ready ecosystem.
Take a deep dive into the technical components of MuleSoft Agent Fabric
MuleSoft Agent Fabric provides the foundation to bring order and structure to your agentic strategy – unifying discovery, orchestration, governance, and observation in one cohesive layer. It helps organizations move from isolated AI experiments to a connected, enterprise-scale ecosystem, one where agents can collaborate safely and effectively to deliver real business outcomes.
If you’re ready to explore the technical side – from architecture to deployment best practices – the MuleSoft Agent Fabric technical overview eBook offers a deeper dive. It’s a practical resource for architects, IT leaders, and anyone looking to make AI work securely and reliably across the enterprise.
