AI has changed dramatically in recent years. Once centred on data-driven machine learning models, it has now entered an era driven by large, pre-trained models and intelligent agents. Today, the focus is on building and orchestrating agentic systems that can reason and act autonomously.
In this expert opinion, Taras Firman shares his insights on this transformation — how the focus of artificial intelligence work has changed, what challenges come with building agent-based systems, and why frameworks like LangGraph have become crucial for structuring and managing modern AI workflows.
How has AI transformed in recent years?
Five to ten years ago, when we thought about AI, we mostly referred to machine learning (ML). It was all based on training models — we needed data; everything revolved around data and models. We had datasets, we trained models on those datasets, and as a result, we obtained AI systems built specifically on a client’s data — models trained directly on it, optimised for solving certain business problems.
If we talk about modern AI, many things — not all, but many — have shifted toward AI that leverages large, pre-trained models. The software development process now largely involves creating agent-based systems. In other words, the focus has moved toward agentic AI.
How has the nature of AI work itself evolved?
Instead of training models, we now focus on building agents. These agents can be based on large language models (LLMs), visual-language models (VLLMs), or even machine learning models but still follow an agentic approach.
When it comes to agents, the key challenge is their structuring, management, and orchestration — and that can be effectively implemented using a framework called LangGraph.
How has LangGraph changed the way you build AI systems?
In our projects, for building various AI-agentic systems, such as orchestrating routing agents, agents of agents (hierarchical agents), and similar systems — we use LangGraph. It has become an indispensable tool, enabling us to design clear pipelines for agents, manage them efficiently, and ultimately deliver a high-quality product.
LangGraph is a framework from LangChain for building stateful, multi-step AI workflows as directed graphs. It allows you to define each step of the process as a node that can run code, call models, make decisions, and carry state forward. This makes it suitable for AI agents, document processing pipelines, and other scenarios where logic involves multiple stages rather than a single prompt.
As AI moves from models to agents, how do we shape its future?
Shaping AI’s future means focusing on agents that can act and reason autonomously, not just on models. Frameworks like LangGraph enable the structure and management of these agents. The challenge now is designing systems that are both powerful and understandable, so AI can truly augment human decision-making.
This shift towards agentic systems will transform industries by enabling workflows that run independently and adapt in real time. Businesses will gain faster execution, fewer manual handoffs, and improved decision accuracy. Companies that adopt agentic AI early will see significant competitive advantages through automation, scale, and smarter operations.
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