Using AI Agents to Turn Thought into Action
Artificial intelligence (AI) agents are advancing rapidly, evolving from systems driven by static rules to ones that can learn, adapt and act autonomously. By continuously analyzing and updating their understanding of the world, these agents can convert insights into real-time, actionable outcomes.
One of the most iconic examples in the psychology of learning is Pavlov’s discovery of classical conditioning. While studying digestion in dogs, Pavlov observed that the animals would begin to salivate not just when food was presented, but also when an assistant entered the room. This revealed a deeper insight into how behaviors could be conditioned by association, which is further explained here: “Based on his observations, Pavlov suggested that the salivation was a learned response. Pavlov’s dog subjects were responding to the sight of the research assistants’ white lab coats, which the animals had come to associate with the presentation of food.”
Pavlov’s discovery of classical conditioning demonstrated how behavior could be shaped through repeated associations. In much the same way, AI agents represent the next evolution of this concept; however, rather than relying on fixed stimuli and responses, AI agents continuously learn and adapt based on prior data inputs. This dynamic form of conditioning allows them to move beyond simple associations, shifting from understanding patterns to implementing actions in real time.
“Imagine every individual having a personalized AI agent, functioning like a virtual programmer, capable of executing complex tasks based on simple instructions.”
Pavlov’s discovery of classical conditioning demonstrated how behavior could be shaped through repeated associations. In much the same way, AI agents represent the next evolution of this concept; however, rather than relying on fixed stimuli and responses, AI agents continuously learn and adapt based on prior data inputs. This dynamic form of conditioning allows them to move beyond simple associations, shifting from understanding patterns to implementing actions in real time.
Former Google CEO and software engineer Eric Schmidt recently illustrated this shift in AI’s capabilities, emphasizing how systems can autonomously learn and evolve. He provided a compelling example: “In the event of a TikTok ban, individuals could instruct an advanced AI system to replicate the app almost instantaneously.” As Mr. Schmidt suggests, one could tell a large language model (LLM) to “create a copy of TikTok, transfer the user base, replicate the music, integrate my preferences, and launch it within 30 seconds. If it doesn’t go viral in the next hour, adjust and try again.” According to Mr. Schmidt, this level of AI sophistication is only about a year away. The idea here is to constantly train the LLM to learn and evolve.
This represents a paradigm shift—moving from arbitrary language commands to arbitrary digital commands. He further explains, “Imagine every individual having a personalized AI agent, functioning like a virtual programmer, capable of executing complex tasks based on simple instructions.”
For AI agents to achieve this level of “text-to-action” functionality, they will require a new form of continuous learning. These agents won’t just understand; they will implement. Leveraging expansive context windows, they will process vast amounts of data simultaneously, iterating and improving in real time. This convergence of intelligent agents and text-to-action capabilities promises to reshape how we interact with technology, transforming natural language into sophisticated digital outcomes.
How Will AI Agents Evolve?
Consider the 1983 film, War Games, where a supercomputer, Joshua, was programmed to learn and master various combat strategies, including one it interpreted as a game: global thermonuclear war! The computer’s ability to learn and adapt from simulations reflected our early fascination with machines that could think and evolve, foreshadowing today’s advancements in AI.
This McKinsey post builds on Mr. Schmidt’s perspective: “The natural-language capabilities of gen AI unveil new possibilities, enabling systems that can plan their actions, use online tools to complete those tasks, collaborate with other agents and people, and learn to improve their performance. Gen AI agents eventually could act as skilled virtual coworkers, working with humans in a seamless and natural manner. A virtual assistant, for example, could plan and book a complex personalized travel itinerary, handling logistics across multiple travel platforms. Using everyday language, an engineer could describe a new software feature to a programmer agent, which would then code, test, iterate, and deploy the tool it helped create.” Furthermore, “A multiagent system could then interpret and organize this workflow into actionable tasks, assign work to specialized agents, execute these refined tasks using a digital ecosystem of tools, and collaborate with other agents and humans to iteratively improve the quality of its actions.”
The workflow begins with the user’s natural language prompt or instructions. The AI agent then translates the prompt into a structured workflow, breaking it down into tasks and subtasks. From there, the AI agent continuously iterates to refine and improve the output. In the final step, the AI agent executes the desired action, completing the process.
As AI agents continue to evolve, their ability to move from understanding to action hinges on the sophistication of their design. Different types of AI agents are tailored to handle varying levels of complexity, from simple reflex-based responses to advanced decision-making processes that involve learning and optimization. Understanding these distinct types of AI agents can help clarify how they operate and what capabilities they bring to real-world applications.
IBM shared these examples of five different types of AI agents:
Simple Reflex Agents:
For example, a thermostat that turns on the heating system at 8:00 PM every night.
These agents operate based on current perception, relying on preprogrammed condition-action rules without memory or interaction with other agents, making them effective only in fully observable environments where they cannot adapt to unexpected situations.
Model-Based Reflex Agents:
For example, a robot vacuum cleaner that senses obstacles and stores a map of the cleaned area to avoid repeated cleaning.
These agents use both current perception and memory to maintain an internal model of the world, updating it as new information is received. They can operate in partially observable and changing environments, offering more flexibility than simple reflex agents, though they are still constrained by preprogrammed rules.
Goal-Based Agents:
For example, a navigation system that recommends the fastest route to a destination, adjusting if a quicker route is found.
AI enables businesses to process and analyze vast amounts of customer data, These agents have an internal model of the world and pursue specific goals by planning actions through searching for sequences that will help achieve those goals. Their ability to plan makes them more effective than simple and model-based reflex agents.
Utility-Based Agents:
For example, a navigation system that chooses a route based on fuel efficiency, traffic and toll costs to optimize the travel experience.
These agents choose actions that not only achieve goals but also maximize utility or reward by using a utility function to measure the usefulness of each action. They select the optimal actions based on criteria such as time, cost, or efficiency.
Learning Agents:
For example, personalized recommendations on e-commerce sites, which improve accuracy as they track user activity and preferences over time.
These agents have the ability to learn from new experiences and autonomously update their knowledge. They are comprised of four elements: learning, a critic (providing feedback), performance (selecting actions), and a problem generator (suggesting possible actions). They can be either utility- or goal-based and improve over time through continuous learning.
The evolution of AI agents is not only reshaping how we interact with technology, but also unlocking new possibilities for adaptive automation. This transformation compels us to rethink how we currently engage with tools like Alexa or Siri for basic tasks. The next generation of AI agents will be personalized, adapting to our habits and behaviors.
Will our AI agents eventually behave much like Pavlov’s dog, anticipating questions or actions as we enter a room? Understanding how these diverse AI agents are trained and their capabilities is crucial for harnessing their potential and navigating the profound impact they are poised to deliver.
Questions? Feel free to email me here. As always, thank you for reading.
