If you’ve been following the news lately, there’s a good chance you’ve seen a few headlines about AI agents. From Salesforce debuting its Einstein Service Agent to Google announcing Project Oscar to startup Thoughtful AI announcing a $20M Series A round to fund its suite of human-capable AI agents, they’re seemingly everywhere. Some are even categorizing OpenAI’s new GPT-4o model as an AI agent.

While the technology is still relatively nascent, it’s made some significant strides over the past year. Many companies in this space are increasingly giving thought to how LLMs can move beyond chatbots and into autonomous agents that perform tasks on their own accord. 

So, are agents truly the next big thing in AI? How do they work and what are some real-world applications? And are they worthy of the hype? In this blog post, we’ll answer those questions and more.

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What Are AI Agents?

There is still a lot of ongoing research and discussion about AI agents, but the definition largely being used today is that AI agents are models and algorithms that can autonomously make decisions in a dynamic world.

“Autonomously” is the key word here. AI agents are not just reactive; they’re proactive. They’re able to make decisions, learn from their environment, adapt to changes, and refine their performance. They operate with a certain degree of independence which allows them to complete complex tasks, such as personal assistance, data analysis, and more. 

AI agents can be either software-based or physical entities. As you might have guessed, software agents run on computers or mobile phones and use apps. Embodied or physical agents, on the other hand, are agents that are situated in a 3D world such as a video game or in a robot. 

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How Do AI Agents Differ from AI Assistants?

Another helpful way to understand what AI agents are is to understand what they are not. Often, AI agents are confused with AI assistants such as Siri and Alexa. The main difference between the two is that while AI assistants perform tasks based on voice commands and prompts, they do not operate independently and are unable to learn and adapt over time.

Another key distinction lies in their applications. Because of their capabilities, AI agents are often employed in sectors where decision-making needs to integrate vast datasets, such as in healthcare for diagnosing diseases or in call centers to predict customer behavior. These agents rely on robust AI models and APIs that interact with extensive knowledge bases. AI assistants, however, are primarily used in personal contexts or as digital assistants in professional settings. They help manage emails, schedule meetings, or even provide suggestions using chatbots.

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How Do AI Agents Work?

AI agents work by following a perception-action loop, which involves several key steps that we’ll break down in this section. For each step, we’ll also provide an example of what it would look like for a popular type of AI agent: an autonomous vehicle.

Perception (Sensing)

In the first step, AI agents gather data from their environment using sensors or data inputs. These inputs can come from various sources, such as cameras, microphones, GPS, or data streams. The quality and type of data collected depend on the agent’s purpose.

🚗 Example: An autonomous vehicle collects data from cameras, LiDAR, radar, GPS, and other sensors to understand its surroundings.

Processing (Data Analysis)

Once data is collected, the AI agent processes it to extract meaningful information. This involves a few steps:

• Preprocessing: Cleaning and organizing raw data to make it suitable for analysis.
• Feature extraction: Identifying important characteristics or patterns in the data.
• Modeling: Using machine learning algorithms to interpret the data.

🚗 Example: The autonomous vehicle’s onboard computer processes the sensor data to identify objects, map the environment, and determine the vehicle’s position.

Decision-Making

Based on the processed information, the AI agent makes decisions. The decision-making process can involve one or more of the following:

• Rule-based systems: Following predefined rules or logic.
• Machine learning models: Using trained models to predict outcomes or classify data.
• Optimization algorithms: Finding the best solution among many possibilities.
• Reinforcement learning: Learning from interactions with the environment to maximize cumulative rewards over time.

🚗 Example: The autonomous vehicle uses the information it has processed to make driving decisions, such as when to turn, stop, accelerate, or change lanes. This involves real-time path planning, obstacle avoidance, and traffic rule compliance.

Action

After making a decision, the AI agent takes action. Depending on the agent’s purpose, these actions can vary. They could be physical, such as moving a robot, controlling a drone, or manipulating objects, or they could be digital, such as sending a message, updating a database, or making a transaction.

🚗 Example: The autonomous vehicle’s control systems execute the driving decisions by controlling the steering, throttle, and brakes.

Feedback & Learning

Many AI agents are designed to learn from their actions and improve over time. Two popular ways for doing this are reinforcement learning (adjusting actions based on feedback from the environment to improve future performance) and online learning (continuously learning and updating models as new data becomes available).

🚗 Example: The autonomous vehicle continuously monitors its performance and learns from new driving experiences to improve its decision-making algorithms.‍

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Overall, while the specific technologies and algorithms used can vary widely depending on the application and complexity of the task, AI agents essentially work by continuously sensing their environment, processing data, making informed decisions, taking actions, and learning from the outcomes to improve their performance. 

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What Are Some Real-World Use Cases of AI Agents?

AI agents are currently being deployed for a variety of use cases, including:

Transportation

Autonomous vehicles such as Waymo or Tesla Autopilot are making informed, real-time decisions to navigate traffic, obey traffic signals, and avoid obstacles in the road. Their goals are to improve road safety, reduce traffic congestion, and provide convenient transportation.

Finance

Algorithmic trading platforms such as Renaissance Technologies or Two Sigma analyze market data, identify trading opportunities, detect fraud, and most importantly, execute trades autonomously. By making rapid, data-driven decisions, they can increase trading efficiency and profitability.

Manufacturing

Industrial robots can assemble products, handle materials, and perform quality control checks autonomously, enhancing productivity and reducing human error. Additionally, AI agents can monitor equipment health and predict failures before they occur, enabling timely maintenance and reducing downtime.

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What Are the Ethical Considerations for AI Agents?

As with any technology, the deployment of AI agents raises important ethical and practical considerations.

The first consideration is around ethical decision-making. Especially in high-stakes environments like autonomous driving or financial trading, it’s important for AI agents to be programmed to make ethical decisions. The people designing these agents should embed ethical frameworks and guidelines into the agents’ decision-making algorithms to ensure responsible behavior in critical situations.

Relatedly, transparency and accountability are also crucial for all intelligent systems, including AI agents. Users and stakeholders need to understand how decisions are made, and there should be mechanisms in place to hold developers and operators accountable for the actions of AI agents. This includes clear documentation and explainability of AI models.

The last one we’ll mention for the purposes of this blog post is legal and regulatory compliance. Regulations like GDPR and HIPAA already exist to protect users’ data and privacy, but new regulations will almost certainly need to be created for AI agents in order to clearly define who is accountable for the actions and decisions they make. Additionally, it will be important to provide mechanisms for individuals and organizations to seek recourse if they are adversely affected by the actions of an AI agent. 

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What Does the Future Look Like for AI Agents?

It should come as no surprise that we believe the future of AI agents is incredibly promising. Not only will improvements in technology allow the agents to become even more independent and learn more efficiently, but AI agents will also be increasingly integrated into workflows across industries. Over time, humans and AI agents will continue to collaborate and eventually find a synergy that enhances productivity, fosters innovation, and creates all kinds of new possibilities.