Dima Raketa: How AI Agents Are Evolving From Tools Into Real Colleagues

(MENAFN- Mid-East Info) Thoughts on automation inevitably take me back to the days when companies were massively adopting chatbots. I was fascinated by the topic myself, but quickly realized that no matter how advanced its NLP, a bot remained a“digital receptionist.” It could respond but not decide. Over the past two years, everything has changed dramatically. Today, we're seeing a completely different class of entities: AI agents. They have access to CRMs, ad accounts and ERPs, and they're capable not just of advising but of launching processes or correcting mistakes. A chatbot used to be the“mechanical voice” of a company. An AI agent is the“head, hands and feet.”

This is an interesting matter, and in this article, I would love to share how smart algorithms are becoming full-fledged colleagues in our industry. How An Agent Integrates Into The Company

It doesn't start with code-it starts with context. Usually, the marketing department uploads a brand book, archives of ad campaigns, crisis history and a list of KPIs into the knowledge base. The result is something like a corporate“cheat sheet” that helps the new employee quickly grasp the company culture. Then the developers step in-not to write millions of lines of code but to connect what's called a Model Context Protocol. Simply put, the agent is given ready-made pipelines to Slack, Salesforce, Google Analytics and other systems. This allows the algorithm to see the business the same way we humans do: where the data lives, what actions are permitted and what's off-limits.

That's exactly what happened in a recent project of ours: We built a smart agent powered by a popular neural network and began“feeding” it data. Colleagues from marketing, sales and analytics departments shared their databases and granted access. A few days later, the agent started taking initiative. In one case, it noticed a spike in similar requests to customer support, correlated that with a drop in branded search traffic and suggested the content team urgently update the FAQ. Previously, this would have required a data analyst to export the data and a project manager to assign the task. Now, the entire chain finishes without human intervention. And that's the key contrast with classic robotic process automation (RPA): We don't prescribe every step-the agent identifies patterns on its own and gradually expands its authority, connecting new APIs as needed.

I've just described the traditional approach to creating such agents, but it can (and certainly will) look very different. Artificial intelligence is a powerful force that can now train itself and“communicate” with other neural networks. That's literally what's happening: We train one model and create a“manager agent” on its basis, which then starts“talking” to others, collecting information, adapting scripts, identifying issues and proposing solutions.

In simpler terms, the agent no longer behaves like a disciplined robot waiting for commands. It gradually transforms into the coordinator of a whole network of supporting models: It sends a request to its language“sibling” to generate a press release, forward the draft to the visual design network and then checks with the analytics microservice on whether the material is even worth publishing. The result is a kind of mini-team, where the manager keeps time and each specialized model plays its part. On a human level, it feels like hiring a universal supervisor: not just someone who advises, but someone who negotiates, synchronizes and, most importantly, learns from mistakes, adjusting processes without needing a new technical brief. Watching an agent evolve, I often catch myself thinking that in the future, we'll hire not for specific tasks but to configure and guide entire orchestras of digital performers. Why This Is No Longer Science Fiction

Skeptics often ask:“Isn't it too early to celebrate?” I respond with three observations. First, the level of today's language models can now handle complex, sequential database queries and generate reports with conclusions, not just text. Second, there's low-barrier infrastructure, where you can now build an agent's working environment using visual tools without involving a full dev team. And third, there's economics. A subscription for the computing power of one agent costs roughly $200 to $300 a month, while the savings in salaries and avoided mistakes can total thousands. When the cost of human inaction exceeds the cost of a proactive algorithm, the decision becomes obvious.

To summarize:

• Mature LLMs
• Low-code tools
• Economic rationale

Here's what this looks like in real-world cases. In marketing, the agent can monitor media 24/7 and is the first to react to negative trends by drafting a press release. In sales, it can listen to calls, update FAQs and automatically adjust scripts. In logistics, it can cross-reference GPS trackers with shipping documents and block suspicious deliveries.

However, removing human control entirely isn't an option. For example, our agent once noticed a drop in CTR and restarted an ad campaign on its own, overspending the budget. We had to implement a two-factor system: Any actions involving money or public communication now require a human manager's confirmation. What's Next?

Looking at current trends, the next step is catalogs of ready-made digital employees. Marketplaces are already emerging where you can hire a recruiter or procurement agent in 10 minutes. Meanwhile, the concept of a meta-agent-a coordinator managing dozens of narrowly focused models-is emerging. As a result, a company's structure will begin to resemble a hybrid: Humans handle strategy, negotiation and ethical oversight, while the digital cohort delivers speed, precision and 24/7 responsiveness.

Based on my own experience, I've concluded that the AI agent is no longer a toy for enthusiasts. It's a new kind of colleague-one that can analyze, communicate and operate within corporate processes. Organizations that manage to properly integrate these agents into their workforce won't just reduce costs; they'll gain a fundamentally new business dynamic. The rest will have to either speed up or settle for playing catch-up.

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