From prototype to production: build the agentic enterprise with IBM webMethods Hybrid Integration
95% of AI pilots stall
Organizations everywhere are betting on AI agents to supercharge growth. CEOs are investing aggressively, yet 95% of AI pilots stall before reaching production.¹
Standalone agents aren’t enough. Scaling requires real-time access to enterprise systems, strong governance and a trusted platform that bridges experimentation with adoption and trust through strong governance.
With IBM webMethods® Hybrid Integration, you don’t just experiment, you operationalize AI.
Agentic enterprise for growth
An agentic enterprise is one where AI agents are embedded across workflows to automate tasks, decide and collaborate with humans—driving productivity and innovation.
Imagine a workforce where humans and AI agents work side by side:
- AI agents automate tasks, monitor complex systems and resolve issues proactively.
- Employees are freed to focus on strategy, creativity and innovation.
- Businesses accelerate growth without adding complexity or risk.
- HR agents simplify employee services.
- IT agents accelerate helpdesk resolution and monitoring.
- Finance agents streamline approvals and reporting.
- Customer service agents deliver faster, more personalized experiences.
AI agents must securely use the latest AI models, as well as enterprise data and functionality. Early agent experiments have relied on innovation through better AI models. But to truly innovate and differentiate, companies need to be able to use the existing enterprise assets in agents.
Integration is the connective tissue that allows agents to work with enterprise data and functionality. Trust can be created by appropriate governance. This aspect comprises governance of the AI models, but also governance over access to enterprise systems.
Realizing the agentic enterprise vision with IBM
To realize this vision, companies need to focus on three critical capabilities.
Using agentic frameworks to build new AI agents efficiently.
Ensuring that AI agents can access enterprise data and functionality. Investments in a composable enterprise architecture are fully used, existing APIs and integration flows are reused. iPaaS is used to expose existing assets for agent consumption (with MCP support as well as, for example, simplified data models).
Ensuring companies can trust AI agents with access to critical enterprise systems by governing both the models they use and the systems they touch—delivering end-to-end traceability, security and compliance. Our AI Gateway governs an agent’s access to large language models (LLMs); our MCP Gateway governs the agent’s access to enterprise systems.
IBM webMethods Hybrid Integration is the foundation for this shift, providing access to enterprise data and functionality through integration and enforcing governance. This approach offers enterprises the tools to transform isolated AI pilots into a resilient, AI-powered operating model.
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IBM is helping clients balance both imperatives: preparing for the opportunities of agentic AI while ensuring reliability and ROI from current systems. ”
Footnotes
¹Challapally, Aditya, Chris Pease, Ramesh Raskar, and Pradyumna Chari. The GenAI Divide: State of AI in Business 2025. MIT NANDA, July 2025.