In a recent episode of the Agentic AI Series titled "From Chatbots to AI Agents: Inside the Agentic AI Revolution," Akhil from Monetizely provides an insightful breakdown of agentic AI and its revolutionary impact on enterprise software. The video examines how agentic AI fundamentally differs from previous technologies by not just providing recommendations but taking autonomous actions to achieve goals.
The Evolution from Passive to Autonomous Software
To understand the significance of agentic AI, we must first trace the evolution of software. The journey began in the 1960s with IBM's time-sharing on mainframes, moved through the PC revolution of the 1980s and 90s, then transformed with Salesforce's introduction of SaaS in 1999, eliminating the need for physical installation media. By the mid-2000s, Amazon Web Services democratized infrastructure access, fueling the SaaS explosion.
Yet, as Akhil points out, "Even SaaS had bottlenecks. You, the user, the human, was still in the loop, clicking buttons, triggering workflows, and making decisions." This human dependency remained a fundamental limitation until recently.
The 2010s brought Robotic Process Automation (RPA), which attempted to automate repetitive tasks by mimicking human actions on screens. However, Akhil identifies RPA's fatal flaw: "its rigidity. Change the layout? It broke. Add a new rule? It simply failed. Ask it to make a judgment call? Just forget about it."
Then came the watershed moment in November 2022 with ChatGPT's release, ushering in the era of generative AI with impressive language capabilities. But even this breakthrough had limitations.
Generative AI vs. Agentic AI: Moving from Reactive to Autonomous
The key distinction Akhil makes is that "generative AI is still fundamentally reactive. You ask, it answers, you prompt, it responds." Agentic AI represents the next evolutionary step.
"Agentic AI doesn't just generate ideas, it pursues goals. It doesn't just complete tasks, it manages them. It doesn't just respond, it takes initiative," Akhil explains.
This distinction becomes clear through his example:
"Generative AI is when you ask 'write a professional email declining tomorrow's meeting.' Now it gives you a nicely written draft. You can review it, you hit send. Agentic AI is when you say 'I need to cancel tomorrow's 3 PM meeting.' The agent looks at your calendar, identifies the meeting, checks who all are attending, analyzes the context, writes the email, taking into account the tone, the stakeholders, the urgency, proposes three alternate slots that work for everyone, sends the message, tracks responses, and then updates the calendar automatically."
This represents a fundamental shift from software as a tool to software as a teammate - a transition from automation to autonomy.
The Four-Step Architecture of Agentic AI
Agentic AI systems follow a specific framework that Akhil describes as the "Perceive, Reason, Act, Learn" cycle:
- Perceive: The agent gathers information from various sources to understand the context.
- Reason: Using a large language model, the agent parses inputs and creates a plan, breaking goals into subtasks.
- Act: The agent executes the plan through API calls, messages, database queries, and other actions.
- Learn: After acting, the agent observes outcomes and adapts accordingly.
"These four steps—perceive, reason, act, learn, and then repeat—that is the agentic feedback cycle," says Akhil.
Real-World Applications and Business Impact
The business applications of agentic AI are far-reaching and transformative. In customer support, agentic AI doesn't just answer FAQs but can resolve issues by checking order status, triggering refunds, updating inventory, and logging everything in the CRM.
In legal contexts, solutions like Harvey are "building agents that don't just summarize case law. They draft contracts, manage red lines, recommend negotiation positions, and then file documents with compliance teams." The results speak for themselves: "In user studies, 97% of lawyers preferred Harvey's output over GPT-4 alone. That's not about better language, it is about better process."
This shift has profound implications for how businesses approach software. As Akhil explains, "Traditional software, you use it. Generative AI, you interact with it. Agentic AI, you delegate to it. That shifts how we design software, how we sell it, how we price it, and then how we measure success."
Market Adoption and Future Outlook
The market is already responding to this paradigm shift. In September 2024, Salesforce launched Agent Force, and Microsoft is integrating agents into Office. Google's Gemini 2.0 is designed specifically for agent orchestration. Meanwhile, startups are raising billions for agentic AI platforms.
According to Gartner data cited by Akhil, "19% of enterprises are making significant investments in agentic AI. Another 42% are experimenting cautiously. By the end of 2025, 80-95% will be using some form of agentic automation."
The implications are clear: "This is not hype. It is a shift in how work gets done. We are moving from software as a tool to software as a teammate."
Embracing the Agentic AI Revolution
The rise of agentic AI presents businesses with a fundamental choice that Akhil articulates powerfully: "Will you build with agents? Will you compete with them? Or will you be replaced by those who do?"
As enterprise software continues its evolutionary journey from passive tools to autonomous agents, business leaders must begin strategizing how to incorporate these technologies into their operations and offerings. The question is no longer if agentic AI will transform business processes, but how quickly organizations will adapt to this new paradigm where software doesn't just assist—it acts.
For SaaS executives in particular, this transformation represents both an opportunity and a challenge: to reimagine products as autonomous agents that can deliver unprecedented value while maintaining appropriate human oversight. Those who successfully navigate this shift will likely define the next generation of enterprise software.