Editorial
Research Report
Designing AI-First Systems for Customer Access
Over the past few years, most professionals have learned how to work with AI through chatbots. We ask a question, get an answer and if it’s not quite right, we try again. That interaction model has shaped our expectations of what it means to work with AI.
Agentic AI introduces a very different experience. Where chatbots are built to respond, agentic systems are built to take responsibility for the delivery of outcomes. Once you embrace that distinction, the shift becomes less confusing and far more compelling.
Table of Contents
- You Can’t Delegate to a Chatbot
- Is Your Agent Really an Agent?
- Measuring Outcomes, Not Tasks
- Why Working With AI Agents Feels More Human
- The New Agentic Experience
You Can’t Delegate to a Chatbot
In a previous article, I wrote about the psychology of delegation and how difficult it can be to let go of control and trust something else to work autonomously on our behalf. That challenge becomes especially clear when we compare chatbots and agentic systems.
Chatbots are reactive by nature. Each interaction stands alone, and every refinement requires you to stay involved. They’re useful for thinking and drafting, but they don’t plan across steps, hold a broader goal in mind or take responsibility for finishing the work. In other words, you don’t delegate to a chatbot — you supervise it.
Related Articles: AI Agent vs. Agentic AI: What’s the Difference — And Why It Matters
Is Your Agent Really an Agent?
“Agent” has quickly become the latest AI buzzword. Gartner predicts that by 2028, at least 15% of everyday work decisions will be made autonomously by agentic AI. But today’s market is mostly noise. Gartner also estimates that out of thousands of solutions labeled “agentic,” only a fraction deliver anything truly agentic.
One reason for the confusion is that agents are often mistaken for automation.
- Automation follows predefined scripts, which makes it efficient, until conditions change.
- Agentic systems are designed differently. They operate within goals rather than scripts, making decisions along the way and coordinating work until an outcome is reached.
Specialization is another defining difference. General-purpose language models are powerful because they can do a little of everything. However, in enterprise settings, that breadth often comes at the cost of depth. Agentic systems take the opposite approach, relying on multiple specialized agents — each with a specific role or domain — working together as a coordinated system.
Some agents plan, others execute and others evaluate and adjust. Humans remain in the loop, but they’re guiding the system rather than micromanaging it. This orchestration is what makes agentic AI fundamentally different.
Measuring Outcomes, Not Tasks
One reason agentic AI can feel unfamiliar at first is that we’ve learned to measure AI by response speed. But tasks and jobs aren’t the same thing.
A task is answering a question or generating a piece of content. An outcome is completing something meaningful, such as preparing a campaign, aligning messaging or moving work forward. Chatbots handle tasks.
Agentic systems are designed to achieve outcomes and goals. That distinction changes how we should measure value.
Let’s use the analogy of planning a vacation. Sure, you can handle everything yourself: researching destinations, comparing flights, reading reviews and building itineraries. Or you can describe your vacation goals to a travel agent and let them orchestrate the details.
The travel agent doesn’t deliver an instant output (and if they did, would you trust it?). But while they’re working, you’re free to focus elsewhere. When you add up the total effort, including research, back-and-forth and decision fatigue, the delegated approach almost always saves time overall.
That’s the same shift with agentic AI. The value isn’t an instant response. It’s absorbing complexity, coordinating multiple tasks in parallel and reaching a goal without constant hand-holding.
Related Article: OpenAI’s Operator in Action: What It Can — and Can’t — Do
Why Working With AI Agents Feels More Human
Prompting is something we’ve learned to do. The syntax and structure we use with chatbots isn’t how we naturally communicate. As humans, we’ve adapted ourselves to the technology.
Delegation, on the other hand, is something we already know how to do. Agentic interaction is goal-based and conversational. You describe what you’re trying to accomplish, not every step required to get there. The context is built in, so you’re not restating it every time or constantly retraining a chatbot.
For time-strapped professionals like internal communicators, this matters. Purpose-built agentic systems already understand brand, tone, channels and governance. You’re not starting from scratch with every interaction. The system works from a shared ownership of outcomes.
Working with agents feels more natural because it isn’t constrained by the prompting strategies we’ve learned over the past few years. While there may be a brief adjustment, agentic AI quickly feels less like operating a tool and more like collaborating with a teammate.
The New Agentic Experience
Upwards of 80% of organizations have tested generative AI in some form, yet very few are seeing meaningful performance gains. McKinsey calls this the “gen AI paradox.” Plenty of motion, not much momentum.
They also suggest agentic AI may be the way out of that loop. Not because it adds more features, but because it changes how work gets done. Agents can think, plan and act toward business objectives. As agentic systems mature, the evaluation shifts from “How fast did it answer?” to “What did we accomplish?”
Webinar
Feb
25
Content Strategy Leaders Live: Scaling for Speed, Complexity and AI in High Tech
A candid roundtable on how high-tech leaders are rethinking content at scale.
Register
Webinar
Feb
26
Do More with Less: Modernizing the Cloud Contact Center for 2026
Learn how to leverage cloud platforms without adding a single hire to personalize every customer interaction.
Register
Webinar
On demand
Cut the Noise: Deploying AI That Actually Moves the Needle
Learn how to turn AI experimentation into concrete revenue operations.
Watch Now
Webinar
On demand
Ditch the Desk Phones: How Modern Teams Drive AI-First Communications
Find out how one team finally pulled the plug on a legacy phone system. And built something smarter.
Watch Now
Webinar
On demand
Rebrand. Migrate. Optimize. How to Do It All (Without Slowing Down)
Cresta leveled up site speed, design flexibility and marketer sanity (in record time). Find out how.
Watch Now
Webinar
On demand
From Manual to Magical: How AI Transforms CX Teams
Learn how to replace manual support processes with automation that actually delivers.
Watch Now
Internal communicators are uniquely positioned to guide this shift. Comms teams understand delegation, trust and alignment, and can model what responsible adoption looks like across the organization.
Agentic AI delivers a new experience. Not because it answers faster, but because it’s built for real delegation and delivering meaningful outcomes.
Learn how you can join our contributor community.