The Agent Economy -Part III: Rise of Agentic AI
*Personal opinion and thoughts based on my observations and experience/ will continue to evolve/update this
Part I: https://medium.com/@gopivikranth/the-agent-economy-6a16cecb707d
I believe agentic AI will represent the single largest shift in how enterprise decisioning is done over the next 5 to 10 years.
1. AI agents are the new decision and intelligence engines
2. Most if not all current decision engines (SaaS) in the market will be reimagined with agentic AI and are likely 50% or more cost effective.
3. Classical (predictive AI), Traditional Data science and Gen AI will converge to agentic AI
4. Multi-modal- Multi agent systems that can perform text to action are manifesting and they will continuously capture more workflows.
5. A new class of use cases which are executed by agentic workflows will emerge as digital workers and re imagine existing mechanisms
Tens of billion-dollar companies will emerge in this space in the next 5–10 years
Artificial Intelligence (AI) has undergone significant shifts in recent years, with the rise of generative AI and it now entering its second innings with Agentic AI. In particular, the future of enterprise solutions lies in the emergence of AI agents as core decision-making and intelligence engines. The evolution of these AI systems is poised to reshape the SaaS (Software as a Service) market, enabling more dynamic, automated, and intelligent workflows.
1. AI Agents are the New Decision and Intelligence Engines
The foundation of the future enterprise will be built on AI agents that serve as decision-making and intelligence engines. These agents go beyond traditional machine learning models, utilizing large-scale language models (LLMs), reinforcement learning, and multimodal capabilities to make complex decisions in real time. As the core intelligence engines, AI agents will power a new generation of enterprise software, offering unprecedented levels of automation, accuracy, and adaptability. AI agents will become ubiquitous in enterprise, solving complex problems at unprecedented speed and scale per recent Nvidia GTC keynote.
Historically, decision-making in enterprises have relied on rule-based systems, human intervention, and manual data analysis. While SaaS tools have streamlined some of these processes, AI agents will push this transformation further by executing high-level reasoning tasks without human input. This means enterprises can move from decision support systems to decision execution systems, where AI agents autonomously optimize workflows, detect patterns, and solve problems.
2. Current decision engines (SaaS) in the market will be reimagined with agentic AI
One of the most disruptive implications of agentic AI is its potential to reimagine how current SaaS decision engines function within enterprise software. Traditional SaaS tools that rely on pre-programmed decision trees or machine learning models are reaching the limits of their capabilities. Agentic AI, with its ability to continuously learn and adapt, will replace these rigid systems with dynamic, intelligent agents that respond in real time to evolving data.
SaaS applications will need to evolve into agentic systems, or risk being outcompeted by AI-native platforms capable of real-time decision-making and action. SaaS platforms today are largely designed for human oversight; however, agentic AI will shift the paradigm toward autonomous systems that are not just executing predefined tasks, but also continuously optimizing and learning from feedback loops.
Imagine the shift in industries like finance, healthcare, and supply chain management, where decision-making has long relied on static models and preset workflows. AI agents will empower businesses to transition from static, predefined rule-based systems to adaptive, real-time intelligent systems. The competitive landscape of SaaS will be reshaped, with leading companies integrating agentic AI into their decision engines even as it cannibalizes their current revenue.
In the next 5 to 10 years, i anticipate the emergence of many billion-dollar companies that leverage agentic AI as their core technology. New firms leveraging agentic AI in sectors such as healthcare, finance, manufacturing, and legal services are particularly well-positioned to capture value. For instance, a healthcare startup that integrates AI agents into clinical workflows could streamline diagnosis, optimize treatment plans, and reduce patient wait times — all while improving accuracy and outcomes. Similarly, in finance, AI agents will automate risk assessment, fraud detection, compliance monitoring and portfolio management at an unprecedented scale.
3. Convergence of Classical (predictive ai), Traditional Data science and Gen ai into Agentic AI
A significant trend underpinning the future of enterprise AI is the convergence of classical AI, generative AI, and traditional data science into a unified framework of agentic AI. While each of these approaches has offered individual benefits, their combination will create AI systems that are far more powerful, adaptive, and capable than any one approach alone.
Classical predictive AI offers structure and predictability, while generative AI provides creativity and adaptability to handle ambiguous tasks. Traditional data science, with its reliance on statistical models and historical data, supplies the rigor needed for accurate forecasting and prediction. When integrated into agentic AI systems, these approaches will deliver superior results, offering enterprises a more comprehensive and intelligent approach to decision-making and task execution. These form the next wave of intelligent systems that can autonomously execute complex tasks in enterprise environment
4. Multi-Modal, Multi-Agent Systems to Capture More Workflows
One of the promising developments in agentic AI is the rise of multi-modal, multi-agent systems capable of performing “text to action” workflows. Multi-modal, multi-agent AI systems can process multiple types of input — such as text, voice, image, or video — and convert them into actionable tasks and outputs.
Multi-modal systems are likely to be the cornerstone of truly intelligent agents that can act and react to complex environments. This capability enables them to capture a broader range of workflows, from customer inquiries to production processes and integrate seamlessly into a variety of enterprise workflows, capturing more value by automating tasks across different domains.
For instance, multi-agent systems can take customer service interactions from initial contact through resolution without human intervention. In a manufacturing setting, these systems can monitor video feeds of production lines, diagnose problems, and implement corrective actions autonomously. This combination of multi-modality and multi-agent architecture allows for much deeper integration of AI into enterprise processes, enabling the automation of tasks that span different data types and communication channels. The potential for multi-agent systems in the enterprise is vast.
5. Emergence of Agentic AI systems as Digital Workers
The evolution of AI agents into digital workers represents a profound shift in enterprise use cases. These AI-driven workflows, capable of performing complex, multi-step tasks autonomously, will reshape the workforce, leading to the emergence of AI as a “co-worker” rather than a mere tool. Gartner predicts that, “one in five workers engaged in non-routine tasks will rely on AI agents for their productivity”
A new class of use cases are emerging in the enterprise world, driven by agentic workflows acting as digital workers. These AI-driven workflows go beyond simple automation to take on more complex, context-sensitive tasks that typically require human judgment.
These digital workers will bring value in industries such as customer service, where AI agents can handle sophisticated queries, resolve issues autonomously, and escalate to human supervisors only when necessary. In manufacturing, AI-driven digital workers will monitor and optimize production lines, reducing downtime and improving quality control. In logistics and supply chain management, digital workers can oversee inventory, manage shipments, and even predict demand fluctuations. These workflows will operate in real-time, creating a seamless flow of operations that requires minimal human intervention and even improve themselves.
This shift will enable businesses to focus human labor on higher-level strategic tasks, while AI handles the repetitive, data-driven processes.
Overall, the rise of agentic AI represents a fundamental shift in how enterprises will approach decision-making, automation, and workflow management. AI agents will become the core intelligence engines of tomorrow’s businesses, enabling more adaptive, efficient, and autonomous systems across various industries. Agentic AI’s potential to reduce costs and enhance efficiency is unparalleled. As this technology unfolds, new companies will emerge that will leverage agentic AI across industries, from healthcare to finance, to revolutionize their sectors
