The digital era is defined by relentless change, where the ability to modernize applications is a prerequisite for innovation, agility, and growth. Over the past few years, generative AI has emerged as a powerful catalyst, automating code generation, documentation, and testing—helping organizations tackle technical debt and accelerate modernization. Yet, as the demands on technology intensify and the complexity of business processes grows, a new paradigm is emerging: agentic AI. This next leap promises not just to assist, but to autonomously orchestrate and execute multi-step workflows, fundamentally transforming how enterprises approach application modernization.
Generative AI—powered by large language models (LLMs)—has already revolutionized software development. It enables teams to:
These capabilities have delivered measurable outcomes: over 50% reduction in modernization costs, 50% fewer defects, and up to 70% faster migration, as seen in real-world modernization projects. Generative AI’s impact is clear—it transforms labor-intensive, error-prone tasks into streamlined, efficient processes, freeing up talent to focus on innovation.
But generative AI, for all its power, is fundamentally an assistant. It generates content, suggests improvements, and automates discrete tasks, but it still relies on human direction and oversight. The next frontier is agentic AI—autonomous agents that can plan, decide, and act across interconnected systems with minimal human intervention.
Agentic AI represents a quantum leap. Rather than waiting for instructions, agentic AI agents can:
Imagine an AI agent that not only refactors code, but also schedules deployments, updates documentation, runs integration tests, and coordinates with other systems—without waiting for a developer to prompt each step. This is the promise of agentic AI: moving from automation to true autonomy.
Transitioning from generative to agentic AI is not simply a matter of upgrading models. It requires a holistic transformation of both technology and organizational mindset.
Agentic AI thrives in environments where systems are modular, API-driven, and cloud-native. Legacy monoliths, siloed data, and brittle integrations are major barriers. Organizations must invest in:
Agentic AI’s autonomy depends on its ability to interact with—and act upon—multiple enterprise systems. This means:
With greater autonomy comes greater risk. Agentic AI must operate within well-defined guardrails:
Agentic AI is not just a technology shift—it’s an organizational transformation. Success requires:
The greatest technical challenge in realizing agentic AI is integration. Most enterprise systems were not designed for real-time, autonomous orchestration. Key hurdles include:
Overcoming these challenges requires a phased approach:
At Publicis Sapient, we are helping clients navigate this transition with a blend of deep modernization expertise, advanced AI platforms, and strategic partnerships. Our proprietary platform, Sapient Slingshot, is purpose-built for the agentic AI future:
Sapient Slingshot is complemented by Bodhi, our enterprise-scale agentic AI platform, which provides:
Together, these platforms enable:
The shift from generative to agentic AI is not a distant vision—it is already underway. Early adopters are gaining a competitive edge by:
Agentic AI will not replace the need for human expertise, but it will redefine roles, accelerate innovation, and unlock new levels of efficiency and value. Organizations that act now—modernizing their technology, processes, and culture—will be best positioned to lead in the age of autonomous, intelligent systems.
Ready to take the next leap in application modernization? Connect with Publicis Sapient to explore how agentic AI, powered by platforms like Sapient Slingshot, can transform your business for the future.