Building Agentic AI with Workato: The Integration Challenges Enterprises Must Address First

AI agents fail in production not because the model is weak, but because the integration layer beneath was never built to enable save operations. By the end of 2026, 40% of enterprise applications will include task-specific AI agents, up from less than 5% in 2025.  Most enterprise integration stacks aren’t ready for that.

Over the past 18 months, Workato has been rebuilding its platform around Enterprise MCP, Agent Studio, and Agent Orchestration, all built on the same iPaaS backbone (Universal Connectivity, API Management, Data Orchestration, Data Hub/MDM) that has made it a Gartner Magic Quadrant Leader for eight consecutive years and a 2025 Forrester Wave Leader.

By examining where enterprise agentic AI initiatives actually stall, we’ve uncovered a consistent set of integration challenges behind nearly every one. Here’s what causes each, and exactly which part of Workato closes the gap.

Enterprise Complexity: The Missing Conversation in Agentic AI

Agentic AI does not operate in isolation; it inherits the complexity of the enterprise it serves. Organizations that establish connected systems, trusted data, enterprise governance, and orchestrated workflows are better positioned to scale AI agents from isolated use cases to enterprise-wide outcomes.

1. Application and data sprawl

Your organization likely relies on multiple applications across CRM, customer support, finance, and operations. Over time, these systems create fragmented data and inconsistent business records that limit visibility across the enterprise.

This poses a challenge for Agentic AI. AI agents can act only on the data and systems they can access. If information is fragmented and lacks shared context, agents inherit those limitations.

Gartner forecasts that through 2027, 50% of critical enterprise applications will reside outside centralized public cloud environments², making unified access to enterprise data and applications essential for AI-driven operations. For organizations seeking to scale AI agents, a trusted and governed view of business data is essential.

Creating a single source of truth with Workato

Workato helps organizations create a single source of truth through Universal Connectivity, which provides 1,000+ native connectors across SaaS, on-premises, and cloud platforms, and through Data Hub/MDM, which maintains governed records of customers, products, and vendors across systems. This shared business context enables AI agents to discover information, make decisions, and execute actions with greater accuracy and governance.

2. Brittle point-to-point wiring and aging middleware

Many enterprises still rely on custom-coded integrations, point-to-point connections, and legacy ESBs to move data between applications. While these architectures supported traditional business processes, they were not designed for AI agents that require real-time access to applications, APIs, and business events.

Gartner forecasts iPaaS to be the fastest-growing segment of enterprise infrastructure software through 2030³, while traditional ESB and B2B gateway technologies continue to decline. This shift underscores the need for integration architectures that are reusable, governed, and built for AI-driven operations.

Modernizing enterprise integrations with Workato

Workato’s integration capabilities and AI workflows replace brittle integrations with low-code Recipes that create reusable workflows managed across development, testing, and production. API Management governs APIs via gateways and developer portals, while B2B/EDI and Intelligent Document Processing modernize partner exchanges and document-driven workflows. Together, these capabilities provide AI agents with reliable, governed access to enterprise systems, eliminating the limitations of legacy middleware.

3. Agents that cannot safely act on enterprise systems

AI agents create value only when they can securely perform actions across enterprise systems. However, many enterprise applications were not built with agent-level permissions, governance, or auditability, creating security risks and enabling uncontrolled AI interactions.

Gartner predicts that by the end of 2026, 40% of enterprise applications will include task-specific AI agents, up from less than 5% in 2025. ⁴ As AI adoption grows, governance becomes a core requirement.

Securing AI actions with Workato Enterprise MCP

Workato Enterprise MCP and MCP Gateway provide a governed entry point for AI agents to interact with enterprise systems. Role-based access controls, audit trails, and secure credential management ensure that agents operate within defined boundaries.

AI by Workato embeds large language model capabilities directly into Recipes, enabling agents to summarize information, classify content, reason about business context, and execute actions within governed workflows.

LevelShift Perspective: Where We Fit: Teams think buying an MCP gateway solves the problem, but they forget that an agent is blind without the underlying workflow recipes. It is like buying a high-tech engine but having no roads. Workato provides the infrastructure, but LevelShift builds the actual integrations and logic that allow the agent to move safely.

4. No shared layer for agents to discover enterprise capabilities

Your systems may contain APIs, integrations, and business processes that AI agents need, but these capabilities often exist as isolated assets across applications. Without a common discovery layer, agents cannot consistently find or reuse them. As organizations deploy more AI agents, they need a standardized way to expose enterprise capabilities and approved actions across systems.

Enabling discoverable enterprise capabilities with Workato

Workato provides this through Agent Studio, where teams can build, deploy, and govern AI agents and agentic workflows. Enterprise Skills package APIs and Recipes into reusable capabilities that agents can safely invoke, while the Agent Knowledge Base provides business context beyond raw API responses. Pre-built MCP Servers for Salesforce, Slack, Jira, Google Workspace, and other applications simplify how agents discover and interact with enterprise capabilities.

5. Multi-agent coordination without a governance layer

As organizations deploy AI agents across multiple business functions, managing them as isolated assistants quickly limits enterprise value.

McKinsey notes that while AI adoption is now widespread, ⁵ relatively few organizations have successfully scaled AI to deliver measurable enterprise-wide impact. The challenge is no longer deploying more agents but coordinating them through shared context, governance, and workflow orchestration so they can operate as a unified system rather than disconnected automations.

Orchestrating multi-agent workflows with Workato

Workato Agent Orchestration coordinates interactions between multiple AI agents under a unified governance framework. Shared observability, audit trails, and workflow controls help organizations manage AI agents consistently across business processes. Otto by Workato extends these capabilities by executing multi-step tasks across enterprise applications with approvals and governance built into every action.

LevelShift Perspective: Where We Fit: Addressing each of these gaps assumes someone has already mapped the existing mess, decided what gets governed first, and built the access model underneath it, and that is rarely something a platform license does on its own. LevelShift is a strategic Workato partner that helps organizations build the foundation for Agentic AI by assessing existing integrations, prioritizing modernization, and designing the Role-Based Access Control (RBAC) and audit frameworks that govern how AI agents access data and act across enterprise systems. The result is an MCP implementation built for security, governance, and enterprise scale.

Accelerating Agentic AI Integration with LevelShift Workato360 Services

LevelShift Workato360 is a structured framework designed that helps organizations accelerate Agentic AI adoption across strategy, architecture, implementation, and governance. The framework aligns integrations, APIs, enterprise data, and AI workflows into a unified operating model, helping organizations modernize legacy integrations, establish trusted data foundations, implement Enterprise MCP, and orchestrate AI agents through governed workflows. This enables enterprises to move from isolated AI initiatives to production-ready Agentic AI with greater speed, governance, and scalability.

Legacy middleware and fragmented data should not limit your AI’s potential. Building safe, enterprise-scale Agentic AI requires an integration layer that is as mature as your models.

Schedule a Workato360 Readiness Assessment with LevelShift today to map your integration gaps, secure your data foundation, and build an Enterprise MCP framework for the future.

FAQs

1. What types of integrations does Workato support?
   Workato supports application-to-application (A2A), cloud-to-cloud, cloud-to-on-premises, API-based, B2B, event-driven, and workflow integrations. Organizations can connect ERP, CRM, databases, SaaS applications, file systems, and legacy environments through a unified low-code platform. This flexibility enables real-time data exchange and end-to-end business process automation.
2. How does Workato compare to other integration platforms?
   Workato stands out with its cloud-native, low-code platform that combines integration, workflow automation, API management, data orchestration, and AI agents in a single environment. Beyond connectivity, Workato enables organizations to automate business processes, govern AI agents, and build agentic workflows, all while reducing integration complexity.
3. How long does it take to implement Workato integrations?
   Implementation timelines vary based on the number of systems, integration complexity, and business requirements. At LevelShift, our team analyzes your architecture, integration landscape, and future goals to recommend the right Workato integration approach. By leveraging low-code development, pre-built connectors, and reusable recipes, we help accelerate delivery and reduce development effort.
4. Can Workato integrate legacy systems with modern applications?
   Yes. Workato can connect modern SaaS applications to legacy ERP systems, databases, file systems, and on-premises environments. With connectors, APIs, and hybrid connectivity options, organizations can modernize workflows and automate processes without replacing existing systems.
5. How does Workato manage APIs and integrations at scale?
   Workato provides centralized API management that helps organizations create, publish, secure, monitor, and govern APIs from a single platform. Combined with reusable recipes and workflow orchestration, Workato enables enterprises to scale integrations while maintaining visibility, security, and governance.
6. How do Workato AI Agents improve enterprise integrations and automation?
   Workato AI Agents are intelligent agents that access enterprise applications, execute workflows, and automate multi-step business processes. With Agent Studio and Enterprise MCP, organizations can build, govern, and orchestrate AI agents that securely connect systems, APIs, and business data. By automating complex workflows, reducing manual intervention, and enabling real-time decision-making across applications, Workato AI Agents make enterprise integrations faster, more scalable, and more efficient.
7. How does Enterprise MCP reduce AI integration complexity?
   Enterprise MCP enables organizations to expose existing integrations and APIs as AI-ready tools without rebuilding connectivity from scratch. This lets AI agents securely access enterprise systems, reuse existing integrations, and accelerate agentic AI adoption while reducing development effort and long-term maintenance costs.
8. How much does Workato cost for enterprise integration?
   Workato pricing varies based on the number of integrations, workflows, recipes, and connected applications, as well as the scale of automation required. Enterprise pricing is typically customized to align with business requirements, integration complexity, and expected transaction volumes.