History of Agents and Agentic Workflows

AI agents and agentic workflows have a rich history, transforming from early concepts to today’s sophisticated systems. This article outlines their development, key milestones, and current applications, providing a clear picture for those new to the topic.

The idea of AI agents started in the 1950s with the Turing Test and the birth of AI at the 1956 Dartmouth Conference. Early examples like ELIZA (1966) showed basic natural language processing, laying the groundwork for future agents.

In the 1970s, expert systems like DENDRAL and MYCIN used rules to solve specific problems, marking early agent-like behavior. The 1980s saw the term “intelligent agent” gain traction, with research focusing on agent-based systems. By the 1990s, software agents handled web tasks, and the 2000s brought machine learning, enhancing agent capabilities. The 2010s introduced deep learning, leading to virtual assistants like Siri and Alexa.

The 2020s have seen explosive growth, with large language models (LLMs) like GPT-3 enabling more autonomous AI agents. These agents now power customer service, autonomous vehicles, and personalized recommendations, with agentic workflows automating complex processes.

An interesting development is the use of AI agents in creative tasks, like generating art or writing, which wasn’t a primary focus in early AI research.

Definitions

AI agents are defined as software programs that use artificial intelligence techniques to perform tasks autonomously on behalf of users or other programs. They interact with their environment, make decisions, and take actions to achieve specific goals. Agentic workflows refer to processes that utilize these AI agents to automate or assist in complex tasks, or a series of tasks, often involving coordination between multiple agents or with the environment.

Historical Background

The history of AI agents is intertwined with the broader history of artificial intelligence, with key developments spanning several decades:

• 1950s and 1960s: The foundation for AI agents was laid with Alan Turing’s 1950 proposal of the Turing Test, evaluating machine intelligence. The 1956 Dartmouth Conference, organized by John McCarthy and others, marked the official birth of AI as a field, coining the term “artificial intelligence.” Early AI programs like ELIZA, developed by Joseph Weizenbaum in 1966, demonstrated natural language processing, simulating a therapist and acting as an early form of an AI agent.

• 1970s: This decade saw the rise of expert systems, which can be considered early AI agents specialized in specific domains. DENDRAL, developed from 1965 to 1983, proposed structures for organic compounds, while MYCIN, from 1972 to 1980, diagnosed infectious diseases and prescribed therapy. These systems used rule-based approaches, highlighting agent-like behavior in decision-making.

• 1980s: The concept of intelligent agents gained prominence, with the term “intelligent agent” becoming more common in AI research. Early theoretical frameworks, such as AIXI proposed as a maximally intelligent agent (noted as uncomputable), were discussed, and agent-based modeling for applications like self-driving cars was explored as early as 2003. The decade also saw the first international conferences on AI, fostering research into agent architectures.

• 1990s: Software agents emerged for practical tasks, such as web crawling and recommendation systems, driven by the growth of the Internet and World Wide Web. The Agent-Oriented Programming (AOP) paradigm was developed, focusing on designing systems as collections of agents. The book “Artificial Intelligence: A Modern Approach” by Stuart Russell and Peter Norvig, first published in 1995, defined AI in terms of agents, solidifying the concept. Multi-agent systems (MAS), where multiple agents interact, also gained attention, with early papers like “Intelligent Agents as a Basis for Knowledge-Based Systems” by Robert A. Stearns in 1988 marking significant contributions.

• 2000s: The rise of machine learning and big data enabled more sophisticated AI agents. These agents could learn from data and adapt their behavior, with applications in areas like personalized marketing and data analysis. Virtual assistants began to take shape, with early prototypes laying the groundwork for later consumer products.

• 2010s: Deep learning and neural networks revolutionized AI agent capabilities, particularly in image recognition, speech recognition, and natural language processing. Virtual assistants like Siri (2011), Google Now (2012), and Amazon Alexa (2014) became mainstream, demonstrating practical applications in everyday life. Reinforcement learning and other advanced techniques were applied, enhancing agents’ ability to make decisions in dynamic environments.

• 2020s: The development of large language models (LLMs) such as GPT-3 (2020) and beyond has significantly advanced AI agents’ abilities to understand and generate natural language. This period, marked by the AI boom, has seen rapid progress, with AI agents becoming more autonomous and capable of performing complex tasks with minimal human intervention. Examples include OpenAI’s GPT-4 for custom AI agents, Google’s Bard, and Microsoft’s Copilot, alongside applications in autonomous vehicles by companies like Tesla and Waymo.

Evolution of Agentic Workflows

Agentic workflows, as processes managed by AI agents, have evolved alongside the agents themselves. Early examples include the use of expert systems in the 1970s for automated decision-making in fields like medicine and chemistry. By the 1990s, software agents automated web-related tasks, and in the 2000s, machine learning enabled more integrated workflows in business processes. The 2010s saw agentic workflows in customer service, with chatbots handling queries, and supply chain management, optimizing logistics. By 2025, agentic workflows are common in industries like healthcare, finance, and retail, automating routine tasks and enhancing efficiency.

Current State and Applications

As of March 3, 2025, AI agents are at the forefront of technological innovation, with applications spanning various sectors:

• Customer Service: AI agents, such as chatbots, handle customer queries, resolve issues, and provide support, often integrated into platforms like Salesforce and Zendesk.

• Autonomous Vehicles: AI agents control self-driving cars, making real-time decisions based on sensor data, with companies like Tesla and Waymo leading the field.

• Personalized Recommendations: AI agents analyze user behavior to provide tailored suggestions, seen in platforms like Netflix and Amazon.

• Data Analysis: AI agents process large datasets to derive insights, used in financial modeling, market analysis, and scientific research.
• Task Completion: As of writing, many tasks which are performed on a computer can be automated with an agentic workflow. Think of any job, any duty, any task… if it involves the manipulation of standard office documents, the crunching of data, research or creative problem solving, an agent or agents can now be used to automated those tasks.
• Multi-Agent Approach: Agents are limited in that if you give them too many ‘skills’ (referred to by developers as ‘tools), they quickly get confused. The multi-agent framework is the solution. Work is delegated to a ‘primary’ agent. The primary agent then decides if it can handle the task on its own. If not, it finds an agent that can perform the task and delegates it. That ‘secondary’ agent returns the work product to the primary agent, which is then passed to the human ‘handler’.

Agentic workflows are increasingly adopted to automate complex processes, such as supply chain management, where AI agents optimize logistics, and personalized marketing, where they tailor campaigns based on consumer data. Research is focused on enhancing agent autonomy, improving reliability, and ensuring safety, particularly in critical applications like healthcare and transportation.

Key Milestones and Technological Advancements

To organize the evolution, the following table summarizes key milestones in the history of AI agents:

This table highlights the transition from rule-based systems to machine learning and intelligent agents, transforming the AI landscape.

Unexpected Developments

An interesting and somewhat unexpected development is the use of AI agents in creative tasks, such as generating art, writing stories, or composing music. Early AI research focused on logical and analytical tasks, but recent advancements, particularly with LLMs, have enabled agents to engage in creative endeavors, expanding their role beyond traditional applications.

Water

Water is a platform that allows humans to interact with AI agents the same way they would with other humans. Water’s “Sidekick” agents perform tasks and communicate like regular human team members. Sidekicks can communicate with, and control, agents anywhere in the world, allowing an organization to extend the skillset of their Sidekicks indefinitely.

Conclusion

The history of AI agents and agentic workflows is a story of continuous innovation, from the theoretical foundations of the 1950s to the sophisticated systems of 2025. As technology continues to evolve, AI agents are poised to play an even more significant role in shaping the future of work, communication, and daily life, with ongoing research promising further advancements in automation and intelligent decision-making.