Embracing the Future: How Agentic Systems are Revolutionising Enterprises
Automation had already begun transforming industries before generative AI came into the picture. Now, the next frontier of innovation is marked by the rise of agentic systems, which are autonomous systems capable of dynamic decision-making, learning from feedback, and executing complex tasks with minimal human intervention. Sriram Gudimella from Tredence shared with AIM some valuable insights into the potential of these advanced systems that are poised to change how enterprises function. The distinction between traditional automation and agentic systems is profound. “Traditional automation is efficient at performing repetitive tasks but lacks the flexibility and learning capability of agentic systems,” Gudimella explained. He emphasised that traditional automation systems require human intervention for updates or iterations, often leading to delays in incorporating feedback. In contrast, agentic systems operate autonomously, continuously learning from real-time data and user feedback, enabling ongoing improvements without the need for human oversight. To simplify this concept, Gudimella likened traditional automation to a chess piece that can only move as instructed, while agentic systems act more like a chess master, strategically assessing the entire board and autonomously planning optimal moves. “A chess piece follows orders, but a chess master anticipates, adapts, and ensures the most valuable outcomes,” he added. This analogy captures the essence of how agentic systems surpass traditional automation by leveraging autonomy and adaptability. Real-World Applications of Agentic Systems Agentic systems are already making an impact across various industries, from commodity trading to gaming, healthcare, logistics, and even agriculture. Gudimella shared a compelling example from the commodity trading sector, where Tredence is helping a client develop an agentic system to make autonomous decisions based on factors like inventory levels, competitor information, and procurement rates. “The goal is to create a system that can scale without human or subject-matter expert dependence, enabling seamless decision-making across multiple geographies,” he explained. Meanwhile, Tredence is also assisting a gaming company in enhancing its decision-making processes. The agentic system analyses data on game performance across different geographies, determining which games and promotions are successful and why. This provides valuable insights that can be applied to future business strategies. Agentic systems are also beginning to show promise in agriculture. “In advanced use cases, these systems are improving productivity and value by streamlining processes and optimising resources,” said Gudimella. The versatility of agentic systems allows them to be adapted for diverse applications, showcasing their potential to transform multiple industries. Accelerating Digital Transformation One of the most exciting aspects of agentic systems is their ability to accelerate digital transformation. “Tasks that used to take weeks can now be completed at the press of a button,” Gudimella noted. These systems break down complex tasks into smaller segments, assigning agents to handle specific elements while orchestrating the entire process. This level of automation not only saves time but also ensures that resources are optimised, enhancing decision-making capabilities within organisations. Agentic systems provide real-time insights by analysing vast amounts of data without the bias that often accompanies human decision-making. “They simulate different scenarios, testing various agents and tools to find the best solution in real-time,” Gudimella explained. This ability to quickly integrate diverse datasets and offer a holistic view allows businesses to make more informed, data-driven decisions. However, despite their promise, the implementation of agentic systems is not without challenges. Gudimella highlighted the need for skilled professionals capable of designing, implementing, and managing these systems. “Not every organisation has the requisite skill sets to handle such complex technology,” he said. Additionally, the cost of deploying agentic systems can be significant, particularly for organisations without experience in managing these advanced solutions. Gudimella also stressed the importance of guardrails and governance to ensure the reliability and accuracy of these systems. While they are autonomous, businesses must establish mechanisms to prevent errors or misuse. “Guardrails are crucial to prevent the system from delivering irrelevant responses or losing the users’ confidence,” he emphasised. Furthermore, ethical concerns surrounding agentic systems must be addressed, particularly regarding data privacy and accountability. When using LLMs in agentic systems, businesses need to ensure that the data used to train these models is ethically sourced and free from biases. “Accountability is a major concern,” Gudimella noted, questioning who would be responsible for the decisions made by autonomous systems. The Future of Agentic Systems Agentic systems are set to play a pivotal role in shaping the future of enterprise automation. Gudimella believes that the rise of small, specialised companies leveraging AI and agentic systems will transform industries. “We are already seeing solopreneurs and small teams achieving phenomenal results with AI, and I believe this trend will continue to grow,” he said. Shortly, companies will increasingly rely on AI agents to handle complex tasks, with fewer employees needed to manage these systems. “It’s all about building an ecosystem where each company provides specialised solutions, integrating with others to deliver comprehensive services,” Gudimella concluded.
Sriram Gudimella from Tredence shared with AIM some valuable insights into the potential of these advanced systems that are poised to change how enterprises function.
“Traditional automation is efficient at performing repetitive tasks but lacks the flexibility and learning capability of agentic systems,”
["Generative AI"]
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["AI", "ML", "generative AI", "Aim", "RAG", "R", "Go", "Git", "API", "GAN"]
Mohit writes about AI in simple, explainable, and often funny words. He's especially passionate about chatting with those building AI for Bharat, with the occasional detour into AGI.