I love automation because it creates capacity to do higher value things, and helps create optionality. My company Good Vibes Only AI (https://gvo.dev) is the realization of that passion. It was founded with the principal of making product experiences that enable humans and organizations be at their 100x best.
Automation has always been a defining force in the evolution of human workflows. Changing how we work, live, and even think about the concept of labor itself. From simple tools to sophisticated machines, humans have always been automating. However to truly learn about the progression of automation, I thought of getting a history lesson. Let’s dive in!
“Yet, while automation became essential to industrial and business processes, the systems were limited to highly structured, rule-based environments.”
The Evolution of Automation: From Mechanization to Intelligence
The Age of Mechanization (1760–1850)
The Industrial Revolution marked the first era of automation, where mechanical inventions replaced manual tasks in agriculture, textile manufacturing, and metal production. Innovations like the steam engine and the mechanized loom revolutionized productivity, enabling workers to output more goods with less effort. Though the systems were rudimentary, the framework for modern automation was laid here: machines doing repetitive, labor-intensive tasks. The next big innovation — electricity came after ~100 years.
The Battery Revolution (1800–Present)
The invention of the battery in 1800 marked a turning point, enabling portable, on-demand energy. Early batteries, like the lead-acid cells developed in the 1850s, paved the way for mass electrification and transportation, laying the groundwork for modern automation. The next great leap came nearly a century later with lithium-ion batteries in the 1990s, which revolutionized portable electronics by offering compact, rechargeable power. Today, advancements in solid-state and sodium-ion batteries promise safer, more efficient energy solutions, especially for electric vehicles. New fast-charging and recycling innovations further signal a shift towards a sustainable, battery-powered future, as society moves from static power to a dynamic, distributed energy landscape.
The Age of Electrification (1870–1920)
With electricity, automation moved beyond basic mechanization into a more flexible, distributed form of work. Electrification enabled the widespread adoption of assembly lines, and industries like automotive production became entirely reorganized. Automation allowed for unprecedented production volume and efficiency, with some of the earliest examples of programmed, repetitive tasks within a controlled environment. Yet, while machines began to replace more complex human labor, they still required constant human oversight and intervention. The next big innovation — computers came after ~50 years.
The Age of Computation (1950–2000)
The invention of computers marked a quantum leap in automation. Programmable machines and robotic arms were now capable of performing precise tasks with a degree of adaptability. Computerized control systems in factories became ubiquitous, and software-driven automation found its way into offices with data management systems. Companies like IBM led the charge in digitizing and automating back-office functions, from payroll to data processing. Yet, while automation became essential to industrial and business processes, the systems were limited to highly structured, rule-based environments. Creativity and context-dependent decision-making remained out of reach. The next big innovation — big deep learning and intelligence came after ~20 years.
The Age of Intelligence (2000–2020)
With the rise of machine learning and, more recently, deep learning, automation gained the ability to analyze unstructured data, make predictions, and improve from past actions. This was a pivotal era when tools like Google’s predictive algorithms, Amazon’s supply chain automation, and the first chatbots demonstrated the potential of intelligent automation. These systems learned patterns but lacked a true understanding of context or intent—traits essential for handling tasks that require nuanced human-like reasoning. The next big innovation — agentive AIs came after just ~4 years!
The Age of Agentive AI (2020–Present)
Today, automation has entered a stage characterized by agentive intelligence—systems that can perform complex, context-sensitive tasks without constant human intervention. This generation of AI doesn’t just follow programmed instructions; it perceives, interprets, and takes autonomous action within defined parameters, adapting on the fly.
The Age of Intelligent Robots
Robots with human like movements integrated with vision and sound AIs are evolving — aiming to replicate human tasks autonomously in the physical world. Companies like Tesla (Optimus) and Boston Dynamics (Atlas) lead innovations, enabling robots to adapt in real-time and handle complex environments. These developments parallel digital AI, edging closer to human-like robotic proxies in daily life.
Let’s look at the state of automation systems today that go beyond traditional automation, introducing sophisticated tools capable of handling complex, nuanced tasks.
Prominent AI Agent platforms and their approach:
Microsoft: Through products like Microsoft Copilot and Dynamics 365, Microsoft has positioned itself as a go-to enterprise solution for integrating AI into everyday business tasks. With its OpenAI partnership, Microsoft offers users tools embedded directly in software like Microsoft 365, allowing employees to automate document generation, data analysis, and workflow management across productivity tools. The tech giant will release ten new autonomous agents designed to augment sales, service, finance, and supply chain teams.This seamless integration into widely used platforms is helping businesses streamline complex processes, making Microsoft a major player in AI-driven enterprise solutions.
Anthropic and OpenAI: Both Anthropic’s Claude and OpenAI’s GPT models power a wide range of intelligent agents that automate knowledge-based tasks, from customer service responses to content generation and complex planning functions. OpenAI, with its GPT-based ChatGPT, has achieved mainstream adoption in customer support and business operations, enabling tasks like summarizing meetings, drafting responses, and interpreting vast data contexts. Anthropic’s Claude models emphasize explainability and user safety, making them popular in sensitive domains like healthcare and legal advisory.
Skyvern: Skyvern employs Large Language Models (LLMs) and computer vision to automate tasks on complex, visually dense web interfaces. Skyvern’s approach uses contextual prompts and computer vision to interact with web elements dynamically, enabling it to adapt to website layout changes without relying on brittle rules like XPath. This makes it highly effective for web scraping, data collection, and e-commerce management across diverse platforms.
Imbue: This company are tackling automation with an innovative approach known as “Mixture of Experts” (MoE) architecture. By segmenting tasks into sub-tasks handled by specialized agents, their frameworks can perform complex workflows with human-like efficiency. This allows for modular automation where tasks can be optimized and scaled according to needs, making MoE architectures suitable for dynamic, high-complexity environments, such as software development and project management.
We are in the era of agentive automation where AI not only follows commands but also reasons, adapts, and acts with contextual awareness. This shift brings a level of flexibility, scalability, and productivity that is transforming industries across sectors, from customer service to legal compliance, and advertising — making advanced automation a vital component of the modern enterprise ecosystem.
The Future of Agentive Automation: What Lies Ahead
Increased Autonomy and Collaboration: Future systems will have even greater autonomy, capable of interacting with other systems and humans in ways that feel increasingly seamless. For example, we might see AI-driven project managers that can autonomously schedule and assign tasks based on resource availability, deadlines, and priority changes, all while coordinating with human team members for approval.
Contextual Intelligence and Real-Time Adaptation: As systems improve, they will likely gain better contextual intelligence, understanding subtleties like intent and mood, even in ambiguous scenarios. For example, a customer service AI might adapt its response tone based on user frustration levels, offering escalated solutions or human intervention only when necessary.
Interoperability and Hybrid Human-AI Workflows: Future agentive systems will be increasingly interoperable, allowing hybrid workflows where human intuition and AI efficiency blend seamlessly. A doctor, for instance, might rely on an AI assistant to analyze medical histories and suggest treatment options, all while retaining the final decision-making power. This hybrid model will enhance human work rather than replace it.
There’s also a segment of vertical models, which is the topic for a different post. Build on!
“The rise of these companies signals a new era in agentive automation where AI not only follows commands but also reasons, adapts, and acts with contextual awareness. ”