
Summary Bullets:
• AI has disrupted traditional developer teams and tasks, and new processes and talent will be required to responsibly implement the intelligent automation and probabilistic nature of agentic systems.
• As enterprises drive towards a mature application landscape that is built using AI and for AI-infused applications, intelligent orchestration and integration are critical.
Although AI offers the promise of greater efficiency across a myriad of enterprise workstreams, one of the use cases with the greatest benefit is application modernization. GenAI’s effectiveness in writing and refactoring code has already been highly touted in mainstream media; less known is its use in other aspects of the software development lifecycle (SDLC). It can be used for discovery, documentation, quality assurance, autonomous testing, intelligent orchestration, and other tasks as well. Furthermore, AI is doing much more than accelerating application development; it is changing how software is engineered. Intelligence and analytics are no longer add-ons that are layered onto existing applications. Today’s applications have intelligence embedded into their workflows and decision logic, essentially creating modern apps that are designed to be AI-first.
In this new world, the role of the developer is expanding. Since cycle timelines are greatly reduced with the use of AI, applications can be updated with greater frequency. Along with tools to improve orchestration, developers are looking for solutions that help them audit agentic AI activities, provide contextual intelligence, offer the flexibility to use multiple LLMs, and provide strong governance.
To best leverage new AI-driven capabilities, developers need to put in place new processes that are staffed with appropriate teams that include humans as well as AI agents. The human team members will increasingly need to focus on quality control that includes processes and tools for validating, auditing, and reviewing agentic AI output. A top challenge among enterprises is creating a testing strategy for agentic processes. Organizations are eager to reduce costs, consolidate vendor relationships, and minimize manual efforts, while at the same time moving towards extreme testing.
To address the disruption caused by GenAI and agentic AI, HCLTech has honed its application development practice. Its new vision and strategy for helping customers modernize applications includes a portfolio that encompasses AI driven modernization, AI native application development, intelligent quality engineering, and integration services. As part of its modernization suite, HCLTech provides AI-led tools for app optimization and modernization; solutions to support cloud assessment and migration strategies; and services to manage technical debt and decommissioning of legacy systems. Its application development capabilities include agentic engineering services, API gateway services, AI native architecture and advisory services, and adaptive UI services. To assist with quality engineering tasks, HCLTech provides advisory services and tools for application, agentic AI, and machine learning testing, which help organizations transition from scripting to autonomous proactive testing. Within its Integration portfolio, it offers assistance with intelligence automation, iPaaS, RPA, business process management, and low-code/no-code solutions.
HCLTech’s solutions are built on the company’s AIForce GenAI and agentic AI platform. It includes tools such as Optimax, which provides visibility into existing operations; ATLAS, which can reverse engineer code and perform regression testing, as well as discover code and move it to cloud-based microservices; QMetrix, an umbrella offering that provides assessment tools; and i-Velocity, which offers integrations via partner solutions from MuleSoft and Boomi.
As enterprises drive towards a mature application landscape that is built using AI and for AI-infused applications, intelligent orchestration and integration are critical. Organizations require robust tools for governance, monitoring, and auditing a complex multiagent landscape. Not only will they need to invest in a responsible AI framework, but they will also need to undertake detailed ROI analysis AI deployments need to demonstrate measurable benefits, with FinOps being a critical part of investment decisions.
AI has disrupted traditional developer teams and tasks, and new processes and talent will be required to responsibly implement the intelligent automation and probabilistic nature of agentic systems. Humans will increasingly focus on providing instructions, implementing controls, and developing strategic plans. IT teams will need to include full stack engineers and professionals with orchestration skills. And finally, IT staff and developers will need to partner more closely with lines of business units to understand specific domain challenges and goals. This reimagining of workflows will impact all levels of the enterprise and require extensive change management.







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