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As businesses increasingly rely on AI-driven systems, we are seeing a trend towards “agentic” software — software that is making some level of autonomous decisions. Even if agentic feels too futuristic to be practical, organizations should get ready to harness this pattern quickly.
At an IDC Directions event on April 2, analysts expressed their belief that agents, rather than copilots, will assume responsibility for development. IDC also predicts that by 2026, “20% of frustrated knowledge workers with no development experience will take charge of transforming how they work by building their own agentic workflows, improving cycle times by 40%.”
And who wouldn’t want their workers to feel more productive and successful? Besides the retention benefits from productive and happy employees, there is also potential for significant business return on investment (ROI) through automation, operational efficiency and enhanced decision-making with agentic applications. However, developing and scaling agentic applications comes with challenges, particularly with iteration speed and cost control.
A Platform as a Service (PaaS) approach can mitigate these complexities by abstracting away the difficulties of deploying, managing and scaling agentic applications. Additionally, Model Context Protocol (MCP) plays a crucial role in ensuring AI systems can operate effectively by managing long-term context and enforcing safe connections to third-party data sources.
Agentic applications are designed to act autonomously, reducing human intervention while optimizing processes. Their ROI stems from three key areas: cost savings, efficiency gains and revenue growth. Further, PaaS is complementary to agentic software because it frees the enterprise to take advantage of repeatability and reduced complexity in the following ways:
Agentic apps are extremely powerful for automating complex workflows. Organizations are finding agentic applications appealing from a business perspective because automation can significantly lower operational expenses through lower error rates and optimized labor costs. Further, developers are usually an expensive resource, so enabling them to focus on high-value tasks is essential for ROI.
If organizations are running agentic applications on a fully opinionated and highly repeatable infrastructure stack, developers can get more consistent environments to innovate more quickly. With a PaaS, organizations can shift developers away from having to manually configure infrastructure for AI agents and toward focusing on agentic innovation.
Agentic applications help improve responsiveness to rapidly changing operational needs. For example, in times of acute increases in customer demand, agentic applications enable organizations to quickly scale to meet demand. These applications can help dynamically address peak needs by increasing workforce capacity with 24/7 availability for seamless scalability and facilitating faster decision-making through real-time analysis.
Adding a PaaS tool also simplifies scaling up and down by reducing the need to scale infrastructure manually. Autoscaling capabilities ensure agentic workloads grow with demand, removing the need for platform engineering teams to be called in at off hours.
Beyond cost savings and efficiency, agentic applications can also drive new revenue streams by providing differentiated customer experiences. For example, if an agentic application enables users to ask for help solving a problem using natural language, they do not need any advanced technical knowledge. The agent can figure out which back-office systems need to be queried to resolve the issue.
And because the agentic application has autonomy to query systems and analyze inputs in near real time, it can also provide recommendations to grow topline revenue from those interactions. For example, organizations can develop personalized customer recommendations based on the agent’s analysis or new AI-enabled products that can be packaged and sold at different price points to new audiences.
This must be supported by a flexible platform with repeatable patterns to ensure agents can effectively serve customers and extend revenue opportunities. PaaS is essential in the age of agents because they ensure dev teams can focus on innovation instead of managing AI infrastructure.
MCP is a framework for designing autonomous applications across languages, platforms and third-party data sources. It is a standard protocol that enables AI agents to maintain, retrieve and apply relevant context from multiple enterprise systems across interactions. It improves AI decision-making by ensuring context consistency, safe access to third-party data sources and compliance.
MCP was introduced by Anthropic in November 2024 and, while not an industry standard, its adoption has risen meteorically in the development community and from AWS, Microsoft and Google.
AI agents need to access data from multiple systems of record to build a context that an AI model can use in multistep problem solving. MCP offers a standardized way to write the glue code to access the system of records in a way that AI agents can consume.
Once an MCP server is developed, it can be used by any AI agent that has an MCP client. For example, if you build an MCP server to access and update data in Jira, then all the agents that need to read and update issues can use the Jira MCP server. Without an MCP server, every AI agent would need to write its own custom glue code to access Jira.
In this recent video, Adib Saikali, distinguished engineer, VMware Tanzu, walks through how organizations might architect MCP clients and servers to perform autonomous thinking patterns.
Despite the many ROI benefits, organizations will face significant hurdles when preparing to deploy agentic applications in production. Any enterprise that adds an AI model to an application requires best-in-class observability so that it can monitor response quality.
Constant AI model monitoring, assessment and application updates are critical to maintaining accurate and optimized outputs. When adding “thinking” patterns with agentic applications, platform engineering teams should prepare for even more frequent updates and redeployments because of the highly contextual nature of agentic applications. A turnkey application platform can facilitate agentic innovation by removing complexity from continuous updates and redeployments, so development teams can focus on agentic innovation.
If you’re looking to build new AI skills, please consider attending our Cloud Foundry Day presentation, Platform Engineering Skills for GenAI and Agentic Training Workshop, on May 13 at the VMware Campus in Palo Alto. Also, be sure to check out the Cloud Foundry Weekly vlog series, which has multiple shows dedicated to AI application delivery.
