Kamlesh Dave - TechStoriess.com

Enterprise technology has evolved through several stages, each focused on improving operational efficiency. Organisations initially digitised manual processes, followed by the implementation of enterprise applications that standardised workflows and enhanced data visibility. Over time, automation platforms further optimised operations by streamlining repetitive tasks and routine workflows. Despite these technological gains, most systems continue to operate within rule-based frameworks and demand substantial human intervention. In this emerging phase, artificial intelligence (AI) is transforming this model by enabling systems capable of operating with greater autonomy, intelligence, and contextual decision-making. At the forefront of this transition is agentic AI. In contrast to conventional automation tools that depend on predefined workflows, agentic systems deploy intelligent software agents. These agents are significantly capable of interpreting data, reasoning through objectives and initiating actions across interconnected systems. Also, they interact with enterprise applications, APIs, databases as well as real-time data streams to coordinate activities dynamically. By integrating reasoning, learning, and orchestration, agentic AI enables organisations to move beyond isolated automation efforts. The outcome is the emergence of autonomous business systems: technology environments that continuously monitor operations, optimise decisions and even adapt to changing conditions with minimal human oversight. Inside Agentic AI: How Autonomous Agents Power Enterprise Systems Agentic AI operates through intelligent software agents designed to interpret objectives, analyse context, and execute actions across digital systems. As compared to standard automation tools that follow fixed scripts, these agents work through an adaptive decision cycle—perceive, plan, act, and learn—allowing them to respond to changing data and operational conditions. The process begins with perception, where agents collect inputs from enterprise platforms, APIs, dashboards, documents, and data streams. This information helps the system comprehend the existing state of operations. Agents then move to the planning stage, where broader goals are broken down into structured tasks and potential actions are evaluated. Once a path is determined, agents undertake tasks. They effectively interact with applications, trigger workflows, update records or coordinate with other digital tools. Meanwhile, agentic systems differ significantly in their capabilities. At the simplest level, reflex agents respond instantly to predefined triggers, making them useful for alerts and routine automated responses. Model-based agents introduce greater intelligence by maintaining an internal representation of their environment. This, in turn, assists them in operating when information is incomplete. On the other hand, advanced utility-driven agents weigh multiple possible outcomes to identify and execute the action that most effectively achieves their goals. Enterprise Impact and the Path to Autonomous Business Systems Agentic AI is altering enterprise workflows across industries. In healthcare, AI agents manage scheduling, triage, treatment guidance and claims. Banks and other financial institutions deploy these AI agents to detect fraud, ensure compliance, process loans, and oversee investment portfolios. Manufacturing firms also depend on these agents to monitor production lines, detect defects, optimise supply chains, and predict equipment failures. Moreover, in customer service, AI agents route tickets, resolve routine enquiries, analyse sentiment as well as support self-service channels, while IT teams use them to monitor networks, detect vulnerabilities, allocate resources, and automate maintenance. The key advantage of agentic AI is its combination of automation with decision intelligence. By handling repetitive tasks and evaluating real-time data, agents reduce manual effort while increasing speed, accuracy, and responsiveness. Forecasts indicate accelerated adoption: 33 per cent of enterprise software can integrate agentic AI by 2028, and 80 per cent of customer service issues could be resolved autonomously. Operational costs are also projected to drop nearly 30 per cent by 2029. As enterprises integrate these capabilities, agentic AI will enable the next generation of autonomous business systems, continuously interpreting data, orchestrating workflows, and optimising outcomes at scale. Article contributed By- Mr. Kamlesh Dave, Vice President at Advaiya Solutions Inc..

Tag: Kamlesh Dave

Agentic AI and the Next Wave of Autonomous Business Systems

Agentic AI and the Next Wave of Autonomous Business Systems

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