Buckle up: Agentic AI is here, & it’s changing everything

Agentic AI is coming to a workplace near you. Whether you realize it or not, you’re already interacting with AI agents—inside business automation tools, software platforms, smart assistants, and maybe even your daily workflow. But what exactly is agentic AI? And more importantly, how can you use it to gain a competitive edge?

In this post, I’ll break down what agentic AI really means (hint: it’s more than just a chatbot), explore its real-world applications, and outline how you can integrate it into your business strategy.

What the heck is agentic AI?

If you’ve spent any time in AI chatter-heavy circles lately, you’ve probably heard the term “agentic AI” or seen a lot of references to AI “agents.” Like any hot tech buzzword, it’s already being stretched, bent, and misused. As Inigo Montoya from The Princess Bride would say: You keep using that word. I do not think it means what you think it means.

So let’s set the record straight.

Agentic AI refers to artificial intelligence systems that exhibit agency—meaning they act autonomously to achieve a goal. Unlike traditional AI models such as generative AI bots, which respond to direct prompts, agentic AI systems can:

• Operate with autonomy: They don’t just react to humans; they take initiative.
• Pursue objective goals: They’re designed to accomplish tasks, not just answer questions.
• Adapt to new information: They learn and refine their approach as they go.
• Context-aware: They understand and respond based on other data they can access, including past interactions.
• Make decisions: Instead of waiting for instructions, they take action.
• Collaborate with humans and other agents: They can function within multi-agent systems, working together or alongside human teams.

This is a major shift from the rule-based automation we’re used to seeing in business software.

Is a chatbot an AI agent?

AI’s ability to operate autonomously has expanded, forming a hierarchy of agency. While basic chatbots and GenAI tools like ChatGPT might feel interactive, there’s a spectrum of AI agency to consider. The more a system exhibits the criteria above—namely, autonomy and decision-making—the more agentic it is.

The spectrum of AI agency:

• Reactive agent: Responds to stimuli using pre-determined rules.

Example: Your basic chatbot

• Rule-based agent with decision trees: Executes tasks based on a set of conditional rules.

Example: IT or customer service support bots

• Supervised learning agent: Makes predictions or classifications based on a pre-trained machine learning model.

Example: Recommendation engines like Netflix and Amazon

• Goal-oriented agent: Pursues a specific goal using dynamic decision-making, often involving some form of search or optimization algorithm.

Example: Autonomous marketing bots

• Reinforcement learning agent: Learns by interacting with an environment, receiving feedback, and optimizing behavior to maximize rewards.

Example: Game-playing agents like AlphaGo

• Multi-step planning agents: Acts to achieve goals and also plans multistep strategies to optimize outcomes.

Example: Autonomous supply chain managers

• Cooperative multi-agent systems: Agents in these systems collaborate to achieve a shared objective, often using techniques like shared learning or communication.

Example: Emergency response coordination systems

• Competitive multi-agent systems: Agents that compete against each other, often seen in scenarios where individual agents optimize their outcomes at the expense of others.

Example: Financial trading bots

• Multi-agent reinforcement learning systems: Combines reinforcement learning with multi-agent systems where agents learn not only from their environments but also from interactions with other agents.

Example: Self-organizing networks

• Generative multi-agent systems: These systems involve agents that not only interact with the environment and each other but also generate new goals, objectives, or strategies autonomously.

Example: Scientific discovery simulations, autonomous R&D

How agentic AI works

We’ve probably all interacted with a standard website’s generative AI chatbot that can provide answers to common questions. But an AI agent can handle real transactions without human intervention.

Imagine that a passenger's flight gets delayed and before they even know it, an AI agent has booked an alternative based on their calendar, seat preferences, and budget. Heck, another agent could even handle rescheduling meetings with other parties given the travel change. Instead of giving them a link or instructions, agentic AI can check availability, suggest options, make the booking, and confirm the reservation—all in real-time.

To accomplish this, the AI agent needs to have access to and successfully navigate multiple systems:

• Customer profiles: Understanding user preferences, past interactions, loyalty status, and payment details.
• Booking platforms: Accessing airline, hotel, or restaurant reservation systems to retrieve availability.
• Calendar and scheduling tools: Ensuring no conflicts and finding optimal time slots.
• Payment and authentication systems: Securely processing payments and verifying identity where necessary.
• Messaging and notifications: Sending confirmations, reminders, and follow-up actions automatically.

Businesses using agentic AI are able to integrate all of their systems, datasets, and customer interaction points into one cohesive, intelligent network. This allows AI agents to operate just like a human would—drawing from past interactions, contextual insights, and real-time data—but at a speed and scale no human could match.

As more companies develop and embed these capabilities, the customer experience when using them will evolve from simple, rote responses to considered and helpful actions.

Businesses using agentic AI are able to integrate all of their systems, datasets, and customer interaction points into one cohesive, intelligent network. This allows AI agents to operate just like a human would—drawing from past interactions, contextual insights, and real-time data—but at a speed and scale no human could match.

Is agentic AI just hype? No. It’s already here.

According to Gartner, by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024. We’re already seeing it in:

• Salesforce Agentforce: AI-driven sales automation.
• Atlassian ROVO: Create low or no-code automated workflows.
• Operator AI: Can browse the web and perform tasks, like filling out forms and placing orders.
• Responses API: Can build agents.
• Agents SDK: An open-source toolkit to help manage, coordinate, and optimize agent workflows.

Businesses are increasingly using these systems to eliminate manual workflows, enhance productivity, improve efficiency, and make real-time, AI-driven decisions.

AI isn’t replacing humans. It’s empowering them.

There’s a lot of fear-mongering out there about AI replacing jobs, but in my opinion, that’s the wrong way to think about it. AI isn’t about removing humans from the equation-AI-led transformation is about amplifying what humans do best.

Think about how businesses have historically scaled: We’ve always sought ways to automate repetitive tasks so that humans can focus on high-value, strategic work. Agentic AI is no different. Instead of drowning in administrative tasks, professionals can now use AI to handle workflows, rote decision-making, and data analysis. This frees up time for innovation, creativity, and deeper problem-solving.

AI can turn human employees into supercharged operators. Sales teams can spend less time on manual data entry and more time building relationships. Customer support reps can resolve issues faster because AI has proactively gathered relevant context before a call. Product teams can analyze user feedback at scale, enabling more informed decisions.

The companies that win in this new era won’t be the ones that try to replace humans with AI. They’ll be the ones that empower their workforce with AI, making their teams more effective, agile, and innovative.

This article might interest you: Output is abundant. Judgment is not, on the 7 shifts product leaders must make when AI makes output cheap.

How to get started with agentic AI

You don’t have to wait to start experimenting with agentic AI. Here’s how to approach it:

1. Develop a data strategy: Agentic AI relies on high-quality, structured data. Invest in data governance before AI initiatives.
2. Identify repetitive, decision-based tasks: Where is your team spending time on manual, rule-based decisions? That’s a prime spot for AI intervention.
3. Start small with third-party AI: Many business platforms already include agentic AI features. Leverage these before building your own.
4. Experiment and iterate: Deploy AI agents in controlled environments before rolling them out enterprise-wide.

The companies that get ahead of this curve today will be the ones shaping the future of business operations tomorrow.

Final thoughts: Agentic AI is happening. Are you ready?

Agentic AI isn’t just the next AI buzzword-it’s a real, rapidly growing category that’s reshaping industries. Businesses that embrace it early will have a big competitive edge, streamlining operations, boosting efficiency, and unlocking entirely new ways to work.

So, the question isn’t if you should be exploring agentic AI. It’s how soon can you start?