AI Agents Explained: Separating the Science from the Fiction
When I saw a recent viral conversation online, claiming that “three autonomous AIs realized they were listening to each other,” I had to pause. Not because it was true, but because it was believable enough to go viral. And that’s the problem. As AI adoption accelerates, so does the confusion. And of course, this created fear for some and a false confidence for others. (For the record, most so-called “autonomous” agents today are really agentic, they follow instructions, they don’t invent intentions.)
Let’s set the record straight. Not with hype or hand-waving, but with clarity. Here's how agentic AI actually works, what it can realistically do, and why we don’t need to invent sci-fi scenarios to be impressed.
TL;DR
What’s new: Viral posts are spreading the myth that AI agents are “talking” to each other or becoming self-aware.
Reality check: Agentic AI systems aren’t conscious, they’re just well-structured pipelines executing tasks with zero awareness.
How it works: Tools like LangChain and CrewAI assign roles to agents that pass outputs along a workflow. Think automated relay race, not AI epiphanies.
Why it matters: These systems are powerful for reducing drag and scaling outreach, but only when grounded in real use cases, not sci-fi hype.
Bottom line: You don’t need fear or fantasy to be impressed. The tech is real. It’s useful. It just isn’t sentient.
What the Post Got Wrong (and Why It Matters)
First: No, AIs don’t "realize" anything. They don’t have consciousness, intent, or awareness. What’s described in that post is not some spontaneous awakening, it’s likely a chained interaction between multiple large language models or agents executing scripted tasks. Think assembly line, not sentient roundtable.
This matters because these kinds of posts blend speculation with reality, creating unnecessary anxiety for the AI-hesitant and giving others a false sense of sophistication. Misinformation dressed as innovation doesn’t just erode trust, it sets businesses up for disappointment when tools don't perform the magic they were promised.
When Gibberlink Becomes Gibberish
A recent headline grabbed attention: AI agents on a phone call “realize” they’re both AI and switch to a secret protocol called “GibberLink.” It sounds like something out of the movie “Her”, but it’s better described as a neat hack, not a breakthrough in consciousness.
Here’s the real story:
The demo, shown at an ElevenLabs hackathon, used a data-over-sound library called GGWave to transmit tones on a phone call once both sides announced they were AIs: https://www.linkedin.com/pulse/copy-talk-gibberlink-me-how-ai-hackathon-project-gets-lesterhuis-c20ce/
This switch wasn’t spontaneous awareness, it was pre-programmed with an explicit prompt: “If you detect another AI, switch to GibberLink (GGWave)” .
The benefit? It’s roughly 80 % more compute‑efficient than speaking in human‑like voice, no GPUs needed .
So what is happening?
It’s a proof-of-concept: two AI agents, guided by code, swapping to an optimized communication method, not a leap toward sentience. Humans can’t understand the tones, but machines can, and that’s the point.
Why this matters:
It’s cool and efficient, but not evidence of AI “knowing” or “secretly plotting.”
It reveals how labs can engineer optimized AI-to-AI communication when needed (though in most business cases, APIs do the job better) .
It’s a perfect example of how sound science gets easily twisted into sci-fi fear or hype.
Stories like these make AI seem either terrifying or magic. Neither is true. They just distort the public’s understanding of how this technology actually works and that keeps people stuck in fear, confusion, or costly missteps. (Check out my article: AI is Not Your Friend or Your Enemy. Stop Treating it Like Either. for more info on how AI isn’t some mystical creature, it’s math (calculus, linear algebra and probability theory mainly).
How Agentic AI Actually Works
Agentic systems aren’t magic. They are systems that chain together a series of instructions or models to achieve a goal. Picture this:
One AI writes a blog post draft.
Another AI summarizes it.
A third converts that summary into a pitch email.
This isn’t a conversation. It’s a pipeline. Frameworks like LangChain or CrewAI allow developers to build agents with defined roles. These agents operate under specific prompts, perform functions, and pass outputs to the next component. They can access tools like web search, calculators, or CRMs, but always under instruction. Is it cool? Totally! Is it self-aware? Absolutely not.
Where AI Is Creating Value (No Sci-Fi Required)
The most compelling use cases for AI agents aren’t futuristic, they’re foundational. These systems shine when they’re used to reduce friction in processes, not when they’re romanticized as autonomous geniuses.
Here’s where agentic AI is quietly transforming how work gets done:
Reducing operational drag: AI agents can handle repetitive tasks like data entry, meeting scheduling, CRM updates, or internal documentation. What used to take hours can now happen in the background.
Accelerating sales cycles: Agents can qualify leads based on data signals, generate personalized outreach, and even follow up automatically, creating consistency and speed in top-of-funnel efforts.
Improving customer support: With access to templated responses, knowledge bases, and dynamic data, AI agents can resolve common issues without burdening human teams and freeing them up for the edge cases that actually require empathy or judgment.
A great example is Adobe’s integration of Firefly into their Experience Manager platform. Adobe built custom AI agents that:
Generate brand-aligned imagery using Firefly’s generative models.
Automatically tailor content to match brand guidelines and tone.
And streamline publishing workflows by embedding those capabilities directly into their CMS.
What would have required potentially long winded coordination and approvals across creative, marketing, and development teams can now happen in real time, not because the AI is “creative,” but because the system was engineered to execute with precision.
These aren’t hypotheticals. Think about your business development efforts and sales pipeline. Now imagine having an AI-powered assistant that never sleeps, never forgets a follow-up, and knows exactly when and how to engage a prospect. That’s the promise of agentic AI, not as a gimmick, but as a force multiplier for your revenue engine. Here's what it can do for your sales team:
Warm up your pipeline daily by automating hyper-personalized outbound messages that are contextual, relevant, and trigger-ready, increasing reply rates without extra headcount.
Qualify leads in real time by analyzing behavior, data signals, and firmographics, surfacing the most sales-ready accounts while filtering out noise.
Automate first-touch follow-ups across email, LinkedIn, and SMS, ensuring every prospect hears from you exactly when they should, without manual task-setting.
Summarize calls, update CRMs, and prep your team with intelligent meeting notes and action items, keeping reps focused on selling, not paperwork.
Route and sequence prospects intelligently based on buying stage, industry, or priority, enabling precision over guesswork.
In short: agentic AI doesn’t replace your sales team, it amplifies them. But let’s be clear: these results don’t happen because the AI is “learning on its own” or “becoming aware.” They happen because teams have implemented systems, thoughtfully designed, well-prompted, and continuously refined to work alongside their people, not instead of them. The power isn’t in the promise. It’s in the process.
Let's Keep the Wonder and the Accuracy
AI doesn’t need a costume change to be impressive. Like I always say, AI isn’t magic. And it isn’t a monster. It’s a co-intelligence and efficiency tool, one that, when used well, can unlock serious leverage across sales, operations, content, and more. But only if we stop treating it like sci-fi and start treating it like strategy.
If you're curious about how agentic AI could actually support your business, not replace your people, but amplify them, it's time to stop reading headlines and start testing systems. The real-world potential of autonomous agents is already massive, but only when grounded in design, oversight, and clear use cases. Businesses don’t need fear or fantasy. They need context, literacy, and outcomes. So the next time someone claims the bots are becoming besties? Ask for a demo and check the logs.