What is Agentic AI & How Can My Business Use It?

Dear Will & AiME,

I keep hearing about "agentic AI." It sounds promising, but also a bit abstract. What does it actually mean, and is it something our business should be thinking about?

— Innovation Director in Atlanta

Short answer💡

Agentic AI refers to systems that can plan, make decisions, and take actions across tools to complete tasks with minimal human input. Businesses can use it to automate workflows, research, and operations, but should carefully manage data use, IP ownership, and system oversight.

Dear Innovation Director in Atlanta,

You're right to be curious—agentic AI is quickly becoming one of the most talked-about developments in the AI space. But as with most new tech terms, it can feel a little slippery at first.

If generative AI is the digital equivalent of asking a really smart intern for help, agentic AI is like hiring that intern to take the project from start to finish, with judgment, planning, and coordination across tools. It's not just generating content. It's deciding what to do next. Let's break that down.

What Is Agentic AI?

Agentic AI refers to systems that can take action on your behalf, rather than just responding to prompts. These tools are:

• Goal-driven: You tell the system what you want (e.g., "file this claim" or "build a knowledge base"), and it figures out the steps.

• Autonomous: They plan, iterate, and make decisions without requiring constant input.

• Multi-modal: They can process different types of data (text, images, code) and work across various applications or APIs.

• Tool-integrated: They interact with software platforms like your CRM, document management, or customer support tools to get things done.

Think less ChatGPT and more "AI-powered digital operations manager."

How Is Agentic AI Different from Chatbots or Traditional Automation?

Traditional AI tools follow a set of predefined rules or respond to direct inputs. They're reactive. By contrast, agentic AI is proactive and adaptive. It chains together multiple steps, reasons about goals, and changes course if things don't go as expected. Think: conducting research, summarizing findings, and then emailing a report, all without a human pressing "go" each time.

Practical Use Cases for Agentic AI in Business

Agentic AI isn't theoretical. Early-stage implementations are already transforming the way companies operate. Here are a few use cases to watch:

• Customer Support Agents

  Instead of just suggesting answers, AI agents can open tickets, draft responses, escalate issues, and track resolution steps.

• Market Research & Competitive Intelligence

  Agentic AI can browse public sources, summarize key competitor moves, and prepare a monthly report customized for your product team.

• Internal Knowledge Management

  It can identify FAQs across teams, pull information from internal docs, and generate answers before people even ask.

• Operations Workflow Automation

  For example: reading incoming emails, identifying requests, checking against a database, and initiating the correct workflow in your internal systems.

IP & Risk Considerations

As promising as this sounds, businesses need to approach agentic AI with the same IP discipline they apply elsewhere:

• Ensure the tools are licensed appropriately and don't expose proprietary data during multi-step processes.

• Clarify who owns the output if agents generate content, code, or decisions.

• Track how decisions are made, especially in regulated industries. Autonomous systems still need audit trails.

• Don't forget contracts: If third-party vendors are using agents on your behalf, include protections for data usage, tool approval, and result verification.

Agentic AI can offer a significant edge in productivity and scalability, but only if it is structured with control and clarity. It's less about "letting the AI run wild" and more about building guardrails for systems that think and act with purpose.

-Will & AiME

Three Takeaways:

• Agentic AI tools go beyond generating content. They plan, take action, and execute multi-step tasks with autonomy.

• Early use cases include automated customer support, research workflows, and cross-platform operations.

• Businesses should ensure these tools are vetted for IP ownership, data handling, and proper licensing.