Not far back, we had infrastructure engineers managing various components like storage, backup, network, security, OS and VMs, and we had experts for each technology area. Several of these technologies were automated with the advent of IaaS and PaaS. This resulted in evolving skillsets into platform engineers and SREs who did not have to worry about the underlying infrastructure as it got virtualized and automated. This model, though, was successful only in highly automated environments; the majority of customers continue to have substantial traditional environments where infrastructure and platform automation are limited. These environments are still managed and operated by conventional teams of engineers.

Most of the time, these environments run traditional back-office applications and customers do not want to invest in modernizing apps and/or underlying infrastructure. They see limited ROI in this modernization and have no choice but to run these environments as is on-premises or in the cloud.

Agentic AI opens two very distinct possibilities for enterprise customers to target these legacy environments. The first is optimizing the support needed for maintaining these legacy applications and the second is fast-tracking application modernizations from these legacy environments.

With Agentic AI, we can now have intelligent agents that can understand the legacy environment context and take advantage of troubleshooting Standard Operating Procedures (SOPs) to provide self-service capability to end users in automating the resolution of their application maintenance asks. These Agentic AI solutions collaborate with multiple agents and each agent is responsible for their unitary tasks using a well-defined playbook, associated tools and enterprise data sources. We at HCLTech have developed a Google native solution leveraging Vertex AI Agent Framework, Dialog Flow and Agentspace to automate legacy application maintenance support ticket resolution using Agentic AI's power.

For customers who want to modernize legacy applications, we have introduced Google Agentic AI capabilities, which help them containerize their existing applications at a reasonably lower cost and higher success rate. HCLTech has built an application modernization agentic solution consisting of an assessment agent, conversion agent and testing agent, all working to accelerate application containerization journeys and help reduce technical debt.

• The Assessment agent goes through the codebase to identify code blockers that are preventing applications from being containerized and provides recommendations on how to address the blocker
• The conversion agent, using the recommendations, auto-remediates the code to fix the blockers
• The testing agent identifies the bugs in the code and provides the fixes for the bugs

Faster technical debt reduction will also augment the move towards IaaS and PaaS as more applications can be run in highly automated containerized environments. As per estimates, organizations can save up to 40% in infrastructure costs and another 30% in software license costs by moving to containerized environments.

This transition opens the door for a second use case for Agentic AI. AI agents can be introduced to manage network, storage, backup and security environments, helping enterprises move towards NoOps. In this scenario, in case of any performance or availability issue, agents will talk to each other and help resolve the problem, much like how SMEs would have done in a traditional environment. This can result in cost savings of 25-30% in infrastructure management costs for enterprise customers.

These agents can also be deployed in traditional environments, but their effectiveness and ROI are questionable.

HCLTech, along with Google Cloud, has launched a host of agents that help in infrastructure and cloud operations automation, resulting in the optimization of costs by up to 25%.

These agents can be deployed out-of-box from the predefined catalog of agents available from HCLTech with a standard agent support model for fine-tuning, security and responsible AI. Further, HCLTech-skilled GenAI squads can be engaged for any customization.