These days, AI Agents, MCP servers, tool calling, memory systems, and autonomous workflows are among the hottest topics in the tech world. People talk about how AI can now use tools, make decisions, and carry out complex tasks on its own. Most of these are genuinely impressive developments.
But whenever I think about these topics, another era keeps coming to mind.
The IRC days.
A large portion of today’s young developers have never used IRC. Some may not have even heard of it. But back when the internet was simpler, slower, and maybe more intimate, IRC was the center of online communities. People gathered in specific channels, chatted, shared knowledge, and built communities.
And of course, there were bots.
Back then, owning an Eggdrop bot was a serious deal. It managed channels, tracked users, responded to commands, and took on various automation tasks. Looking back today it seems funny, but even a bot returning the right answer to a specific command was enough to excite us.
A user would join the channel.
The bot would say welcome.
Someone would type a specific command.
The bot would return a predefined answer.
It would give the time, show the weather, remind people of the channel rules.
By today’s standards they were extremely simple systems. But back then, setting them up and managing them was anything but easy.
We couldn’t find most things as ready-made packages. There was no Stack Overflow. There was no AI. When you ran into a problem, finding the solution sometimes took days. A single-line error in a TCL script could keep the bot down for hours. Log files were examined line by line, solutions were searched on forums, help was asked in other IRC channels.
Looking back now, I see an interesting similarity.
Today’s AI Agents and the bots of that era have more in common than we’d think.
Both take an input.
Both run a certain logic.
Both perform an action.
Both produce a result.
Of course today’s systems are far more advanced. Instead of pre-written rules, we now use large language models. Instead of fixed answers, we can generate natural language. Instead of static command lists, we design systems that can call tools.
But the core idea is actually very familiar.
A message arrives.
The system evaluates.
A decision is made.
An action is performed.
A response is produced.
When we talk about MCP servers and tool calling today, I sometimes remember the old IRC services. Back then, too, there were different services, different tasks, and automation layers that interacted with each other. Of course they’re not technologically the same thing. But the problem space is surprisingly similar.
I don’t think the real big change is here.
The real big change happened in access to knowledge.
In the past, when we had a problem, we had to do research to find the solution. Sometimes for days. Sometimes for weeks. Knowing about a topic was an advantage in itself.
Today, reaching information often takes seconds.
That’s why the value of developers has started to change too.
In the past, “knowing how to do it” was what mattered.
Today, “knowing what needs to be done” is becoming more important.
Because access to technical knowledge has been democratized.
AI can write code for you.
It can summarize documentation.
It can explain error messages.
But you still decide which problem to solve.
Maybe that’s why the AI Agent revolution doesn’t feel like a completely new era to me.
It looks more like the next stage of an evolution that has been going on for a long time.
There’s a line stretching from IRC bots to web bots, then to chatbots, and now to AI Agents. Each generation became more capable than the last. But the core purpose never changed.
We were always trying to make computers a little more useful.
Maybe what we’re living through today isn’t a completely new story.
Maybe it’s the natural continuation of the first Eggdrop script we wrote years ago in an IRC channel.
Only now, the bots can really talk.