Matvii Diadkov, Bitmedia.IO founder and crypto geek. He also launched numerous Web3 gaming projects and holds a Master in Computer Science.
If you think robotic brains were first imagined in Terminator or that AI became useful only with ChatGPT, think again. AI problem-solving algorithms appeared as early as 1950.
I’d like to outline the true history of AI agents and how they have been integrated into our world far earlier than you might expect.
By understanding its historical roots and strategic value, I believe business leaders can be more prepared to understand this tool’s future trajectory and better harness it to enhance operational efficiency and long-term profitability.
The First Ideas And Developments
In the mid-20th century, Alan Turing published his paper “Computing Machinery and Intelligence,” in which he posed the question: Can machines think? Later, the Turing Test became the standard for evaluating how well machines could imitate human-like behavior.
AI was first recognized as a distinct scientific discipline in 1956 at the Dartmouth Conference. Two years later, Frank Rosenblatt laid the foundation for neural networks by developing the first artificial neuron models, inspired by biological neurons. His work became one of the earliest examples of machine learning algorithms.
By the 1970s, AI found its first practical applications in biomedical applications with systems like DENDRAL, which analyzed chemical compounds, and MYCIN, which helped diagnose bacterial infections. The 1980s marked a shift from rigid rule-based models to adaptive systems, thanks to the development of backpropagation, which significantly improved neural network training.
The term “intelligent agent” emerged during these decades, referring to a new type of AI system capable of adapting, learning from data, and making complex decisions in changing environments.
Global Automation, Big Data And AI Agents Widespread Adoption
We saw a major turning point in AI development with the rapid rise of deep learning in the 2010s when neural networks began outperforming humans in image recognition and natural language understanding. Here are some key milestones:
• 2010: Siri debuted on the iPhone 4S as the first mainstream voice assistant with natural language capabilities.
• 2012: Geoffrey Hinton’s team demonstrated that deep neural networks outperform traditional algorithms, paving the way for advanced AI in speech, image and text analysis.
• 2014: Amazon launched Alexa, a voice assistant that controls smart devices, orders products and conducts online searches.
I think one of the most underrated early applications of AI was the web crawler. While it may seem primitive by today’s standards, at the time, it was a groundbreaking step toward AI-powered search engines and recommendation systems, especially in indexing information and enabling the collection of vast amounts of data from webpages.
Similarly, IBM’s Watson is often remembered solely for winning Jeopardy!, a popular quiz show at the time. However, I think its true significance lies in demonstrating how AI could quickly process and synthesize vast amounts of information to give direct answers to questions.
Today’s Generative AI And Autonomous Agents
Since the 2020s, AI agents have moved beyond specialized roles and developed more general, autonomous capabilities. For example, OpenAI’s GPT-3, released in 2020, showcased the potential of large language models to generate human-like text. By 2022, generative AI systems like ChatGPT and MidJourney had become an integral part of content creation and inspiration for creators.
Since the pandemic, we witnessed the rise of autonomous AI agents capable of performing complex tasks with increasingly minimal human intervention. Now AI-powered hedge fund agents are makng real-time trading decisions, delivery robots are navigating cities with little oversight using AI-powered drones and self-driving taxis are operating without drivers.
Meanwhile, AI-driven medical diagnostics, such as MedPaLM and PathAI, started providing expert-level analysis, further advancing the role of AI in healthcare.
In 2025, I see AI agents becoming even more autonomous in their ability to interact with their environment, gather data and make complex decisions. I think these systems will continuously gather data, refine their understanding of complex patterns, and make high-level decisions with minimal human intervention.
AI Agents Use Cases
AI agents now blend so seamlessly into our lives that it’s often hard to tell if a service comes from a human or a computer. Use cases across industries include:
• In finance, where they can predict stock movements, assess credit risk and detect fraud, helping organizations make faster, data-driven decisions.
• In marketing, where AI systems can automate content creation, personalize advertising and enhance customer interactions through chatbots and virtual assistants.
• In cybersecurity, where AI is being used to detect threats, patch vulnerabilities and analyze network traffic, providing real-time protection against attacks.
But these advancements come with some challenges. Take their use in marketing, for example, where I find AI agents still struggle to interpret the emotional nuances of certain conversations. In some cases, interactions may feel a bit robotic or lack the human touch that followers expect and even need for emotional health.
However, the overall potential for AI to take on much of the workload of social media management is truly groundbreaking, and despite these current hurdles, many creators are eager to see where this technology will lead in the future.
AI In Healthcare
Past finance, marketing and cybersecurity, I think the industry where AI agents have had one of the most significant impacts is healthcare. AI-powered systems are now crucial for disease diagnosis, drug discovery and virtual medical assistance.
For example, AlphaFold, developed by DeepMind, predicts protein structures, revolutionizing drug development while PathAI aids in the early detection of cancer using computer vision. Babylon Health offers AI-based medical services that assess symptoms and provide diagnoses.
As can be seen, AI agents have evolved from simple expert systems to autonomous solutions for not only healthcare but across business, manufacturing and everyday life.
Although a Goldman Sachs report predicts AI could replace 300 million full-time jobs, I do not think AI agents will ever fully replace human agents. Despite advances, human qualities like empathy, creativity and the ability to make complex decisions in uncertain situations remain essential and irreplaceable.
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