How to build scalable agentic AI applications for enterprises – cio.com

The automation technology landscape has evolved from rule-based automation to machine learning to generative AI to agentic AI. Agentic AI has emerged as one of the frontier technologies that can help enterprises achieve breakthroughs and realize value across the organizational value chain. As we move from single agents to multi-agent systems, organizations can accomplish much more complex workflows quickly and efficiently.
What sets agentic AI apart is its ability to perform complex, multi-step tasks autonomously. This trend matters because agentic AI will reshape how work gets done and organizations need to adapt to a future where their employees must work with AI agents as their co-workers. As with any emerging technology architecture, agentic AI poses unique challenges to engineering teams to build, scale and maintain. The article outlines a platform-centric approach to building and deploying agentic applications at scale, based on experience working with practitioners across several Fortune 500 enterprises.
Most agentic AI use cases have at least four core components that need to be stitched together:
Engineering teams who build agentic applications experience significant effort and time to scale, support and maintain these components due to several factors listed below.
The above list highlights the considerations while implementing Agentic AI at scale, to balance between costs, performance, scalability, resiliency and safety.
As AI and agentic AI applications become a foundational capability for enterprises, these have to be treated as mission-critical systems. AI Engineering leaders within enterprises are starting to put these components together using a platform approach with an LLM gateway (also known as an AI gateway) acting as the central control panel to orchestrate their workloads across models, MCP servers and agents. In addition, an LLM gateway helps implement guardrails, provides observability, acts as a model router, reduces costs and offers a host of other benefits.
In other words, an LLM gateway helps with increasing resiliency and scalability while reducing costs. Given that most enterprises are moving towards a multi-model landscape, LLM Gateways will play a critical role in increasing reliability, mitigating risks and optimizing costs according to industry analysts and experts.
A recent Gartner report titled ‘Optimize AI costs and reliability using AI gateways and model routers’ summarizes the trend in the following way: “The core challenge of scaling AI applications is the constant trade-off between cost and performance. To build high-performing yet cost-optimized AI applications, software engineering leaders should invest in AI gateways and model routing capabilities.”
Industry insiders such as Anuraag Gutgutia, COO of Truefoundry, (an AI platform company), say “Organizations, particularly the ones that operate in a highly regulated environment, should realize that the absence of such a platform exposes them to several risks across the layers. The lack of a robust LLM gateway and a deployment platform on day 1 has a significant impact on speed, risk mitigation, costs and scalability.”
For example, there is a risk of PII leakage externally if guardrails are not implemented. Model upgrades could take weeks without an LLM gateway’s unified API’s. Deployment of each of the components could be broken or siloed, causing re-work and delays without a deployment platform capability.
A LLM gateway, in its basic form, is a middleware component that sits between AI applications and various foundational model providers. It acts as a unified entry point, much like an API gateway that routes traffic between the requester and the backend services. However, a well-architected LLM Gateway offers many more features by abstracting complexity, standardizing access to multiple models and MCP servers, enforcing governance and optimizing operational efficiency. Support for MCP servers enables AI agents to discover, authenticate and consistently invoke enterprise tools and functionality.
The diagram below provides a view of a typical gateway:
Hari Subramanian
The key architectural considerations for a best-in-class gateway should be high-availability, low latency, high throughput and scalability.
A deployment platform with a central LLM gateway can provide several benefits to the enterprise, such as:
These capabilities help enterprises not only to address the challenges of building and deploying agentic AI applications at scale but also to maintain and keep the infrastructure up to date.
Agentic AI is redefining the automation landscape within enterprises. However, the design and maintenance of these systems pose multi-dimensional challenges and complexity as outlined above. A mature architectural approach that includes a platform-based approach on day one goes a long way in avoiding pitfalls and ensuring scalability and safety. A well-architected LLM gateway is a foundational infrastructure component that helps with orchestration, governance, cost control and security adherence.
Disclaimer: The author is an advisor to Truefoundry. 

This article is published as part of the Foundry Expert Contributor Network.
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Hari Subramanian is the co-founder and CEO of Jeeno Technologies, an AI services and platform company that focuses on building responsible AI systems and business process automation. Previously, he led engineering for the artificial intelligence center of enablement at a Fortune 15 global healthcare organization in the US. In this role, he leveraged generative AI to deliver business value at scale to drive business growth and operational efficiencies. As an early adopter of AI governance practices, Hari led the building of a technical platform for AI governance across the enterprise.

During his career at this enterprise, he has led data integration, data engineering and claim platform engineering functions to drive digital transformation. Hari also brings a wealth of technology consulting experience across healthcare, life sciences and financial services domains. He has advised several start-up and scale-up enterprises on strategy and operations. Hari holds a bachelor’s degree in engineering from Anna University, India. In addition, he has certifications in executive leadership from Cornell University and in application of generative AI from MIT.
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