The Rise Of AI Agents: The Next Step In AI Transformation

Forbes

2 days ago

Ashar Samdani: CEO of Code District, a Washington-based software development firm that focuses on helping businesses incorporate modern tech
Out of all the cinematic representations of intelligent agents, my favorite remains Tony Stark's JARVIS. It wasn't just a voice assistant—it was a thinking, learning, proactive system that made decisions, predicted needs and worked seamlessly across tools and interfaces. Back then, it felt like science fiction. But fast-forward to now, and we're closer than ever.
We've already lived through the first phase of AI transformation—where businesses experimented with automation, language models and machine learning to improve tasks, productivity and outcomes. It showed us what was possible.
In just three years since AI became widely accessible, we've entered a new era. One where AI drives decision-making. One where agentic AI doesn't wait for instructions—it takes initiative. And that, for every forward-thinking business leader, opens an entirely new frontier.
As someone who's spent nearly two decades in the tech industry—and has had a front-row seat to multiple AI implementations over the last four years—I can tell you this: every new wave of innovation brings a learning curve. But agentic AI? It's not as complex as it sounds. In fact, it's surprisingly intuitive once you break it down.
This is my attempt to do exactly that—to demystify agentic AI, explain the business value it can unlock and offer a practical guide to help leaders like you take the next step in your AI transformation journey.
What Exactly Is An AI Agent?
An AI agent is a system that can perceive its environment, make decisions and take actions autonomously to achieve specific goals—like a human assistant, but powered by AI. Think of Siri: you ask it to set a timer or play a song, and it responds. But imagine if Siri could proactively schedule meetings, follow up with colleagues and update your calendar—all without you asking.
A true AI agent won't take you through interfaces of different apps—it will abstract away the complexity. You'd simply say, 'Plan a business trip to Europe,' and the tool would check your calendar, compare deals based on your preferences and deliver an itinerary, without showing you the tools it used.
What powers this shift is a combination of technologies: natural language processing (to understand and generate human-like responses), large language models (for reasoning and contextual understanding), machine learning (to improve from past interactions) and orchestration tools (to connect with apps and perform tasks). When these components work together in a closed-loop system, the result is an AI agent that's capable of taking initiative, reasoning independently and completing complex tasks.
The Business Benefits
Here's how companies are already benefiting:
• 24/7 task execution: Agents can autonomously handle routine operations—like report generation, scheduling or customer support—without human intervention or downtime.
• Faster decision-making: Agents analyze data in real time and act on it immediately, reducing delays caused by manual reviews and approval chains.
• Cross-app efficiency: Instead of jumping between dashboards, an AI agent can retrieve info from a CRM, update an ERP and send out an email—all from a single command.
• Reduced operational costs: By automating low-value, repetitive tasks, businesses can free up human talent for strategic work while cutting overhead.
• Hyper-personalized experiences: From sales outreach to supply chain adjustments, agents can tailor responses and actions based on user behavior, preferences or context.
Industry Application: Manufacturing
Here's how different types of agents could apply to various business functions in manufacturing:
• Reactive agents for quality control: These agents respond to immediate inputs, learning from the past. Think of a visual inspection system that flags defects on the assembly line based on fixed criteria—fast, rule-based and focused on the present moment.
• Proactive agents for maintenance scheduling: Proactive agents analyze real-time machine data and usage patterns to predict when equipment will need servicing.
• Hybrid agents for inventory and supply chain: By blending real-time responsiveness and historical insight, hybrid agents can optimize restocking decisions. They respond to order spikes while using trends to anticipate future demands and supplier constraints.
• Utility-based agents for resource allocation: Utility-based agents evaluate competing priorities to find the best outcome. In manufacturing, they might assign tasks across machines or teams based on efficiency, output targets and cost.
• Learning agents for process optimization: These agents learn and improve over time. For instance, they might analyze operator techniques to optimize workflows or identify energy-saving patterns—continually improving plant performance using reinforcement learning.
Common Challenges
For many businesses, the biggest hurdle with agentic AI is knowing where to begin. It sounds powerful (and let's be honest, a bit magical), but without a clear understanding of how and where it fits into your operations, it becomes just another buzzword.
Even when companies do take the plunge, they sometimes fall into the trap of the 'bolt-on' approach—slapping AI on top of flawed or outdated processes in hopes of a quick fix. But agentic systems amplify what's already there. If the underlying workflow is broken or inefficient, AI won't solve it—in fact, it can make things worse by reinforcing bad habits at scale.
And perhaps the most underestimated challenge? Getting people on board. New tech can trigger resistance. Without proper onboarding, training and internal advocacy, even the most promising systems risk being ignored, misused or rejected by the very teams they're supposed to empower.
Integrating AI Agents
Integrating AI agents into your business is about solving real operational problems. To make AI agents actually deliver value, approach implementation like a business transformation.
Pinpoint where things consistently break down—be it slow approvals, clunky data entry or decision-making delays. Don't automate noise. Eliminate it.
Lay out each step of the process you want to improve. Flag areas where agents can make decisions or take action with little human input.
Match tools to tasks. A reactive agent might be great for handling support tickets; a learning agent might be better for optimizing demand forecasts.
Test the agent with live scenarios—not ideal ones. Measure responsiveness, accuracy and user friction.
Treat the agent like a new hire. Gather user insights, monitor performance and tweak continuously for better results.
Smart implementation starts with purpose—not with hype. Focus on impact, and the ROI will follow.
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