AI agents – The Comprehensive Guide

Since the release of ChatGPT, there has been a surge in interest in AI automation. When it comes to automation, AI Agents take the first seat. From Robots to self-driving cars to software systems, AI agents hold the potential to transform our world as we know it. With the continuous improvements in frontier AI models, these agents are becoming more capable and versatile.

However, despite all the hype and speculation, we are still in the early era of AI Agents, and building reliable and useful agents is challenging. A significant amount of effort is being dedicated to developing infrastructures, AI architectures, frameworks, and tooling ecosystems for creating reliable agents. This is similar to the early 90s era of the internet when foundational technologies were being built to support the massive growth and innovation that followed. As we stand at the cusp of this transformative era, now is the perfect time to learn about AI, AI agents, and the tools driving this revolution.

This article will explore what AI agents are, the different types of agents and their workflows, and provide real-world examples, and resources to help you build your own AI agents.

Learning Objectives

  • Understand what AI agents are.
  • Explore different types of AI agents.
  • Discover the key components of AI agents.
  • Learn about AI agent workflows.
  • Explore practical use cases of AI agents with examples.
  • Find out how Composio can help build reliable and useful AI agents in the wild.

What are AI Agents?

AI agents are systems powered by AI models that can autonomously perform tasks, interact with their environment, and make decisions based on their programming and the data they process. The agents can receive input from their environment via sensors or software integrations, and with the help of the decision-making prowess of AI models, they can act to influence it. The input data could be texts, images, audio, or videos. The AI model, typically an LLM (Large Language Model) or an LMM (Large Multi-modal Model), is responsible for interpreting the data and taking the necessary steps to achieve a given task.

Example:

Consider a customer service AI agent for an e-commerce platform. This custom AI agent uses an LLM to understand customer queries received through text messages. When a customer asks about the status of their order, the AI agent interprets the text input, retrieves the relevant information from the order database, and provides an accurate response. If the query involves a product return, the agent can initiate the return process by interacting with the return management system, providing the customer with instructions and updates.

What are the key principles that define agents in AI?

You must be wondering, Isn’t software doing the same thing, that is autonomously completing pre-determined tasks? So, what is the difference between AI agents and traditional software?

AI agents run on powerful LLMs like GPT-4. These models are trained on human-generated data, including logical reasoning, math, and coding tasks. This enables them to understand context of the questions, make informed decisions, and adapt to new information in ways traditional software cannot.

For instance, OpenAI’s Figure robot is a humanoid robot that uses a multi-modal model to reason and execute tasks. The robot processes auditory and visual data from surroundings via the multi-modal AI model. The model then intelligently decides which course of action to take to accomplish a task. The agent does not need human guidance at every step of decision-making, it can take cues from previous states to plan further.

Types of AI Agents

Now that you know, what AI Agents are, let’s dig a bit more and understand different types of AI Agents.

1. Simple reflex agents

The most basic AI Agent whose functionality is limited to pre-defined rules. The agent receives external stimuli via sensors and responds with a specific action based on condition-action rules.

  • Example: In a thermostat, when the temperature drops below a certain threshold, it turns on the heater. It doesn’t store past data or learn from new information.

2. Model-based reflex agents

These are similar to simple reflex agents but unlike the latter, they have advanced decision-making capabilities. Instead of following pre-defined rules, model-based reflex agents use an internal model of the world to understand the effects of their actions, allowing them to make more informed and flexible decisions.

  • Example: a vacuum-cleaning robot. It maintains an internal model of surroundings while cleaning. Sensing dirt cleans the spot; when it sees an obstacle, it updates its map and chooses a new path.

3. Goal-based Agents

Goal-based agents are a step up from reflex agents. The agents are motivated by a specific goal. The agents evaluate multiple actions based on how well they help achieve the goal. The agents can plan ahead of time and take possible sequences of actions to accomplish the goal.

  • Example: a self-driving car that navigates from point A to point B.

4. Utility-based Agents

Utility-based agents possess a sophisticated decision-making framework. These agents can evaluate the effectiveness and desirability of different outcomes. They assess various possible courses of action to complete a task and select the one that maximizes utility. Utility factors can include efficiency, cost, time, and risk.

  • Example: An investment trading system that manages a portfolio of stocks. Instead of just aiming to increase the portfolio’s value (a goal), it evaluates potential trades based on their expected return and risk (utility).

5. Learning Agents

Learning Agents as the name suggests learn from their past interactions to improve at a given task over time. It uses a problem generator to simulate new tasks, that help refine their decision-making abilities and adapt to new situations. This continuous learning process allows them to become more efficient and effective in their operations.

  • Example: A social media recommendation engine starts by recommending popular content and over time, it starts recommending content based on previous interactions.

6. Multi-agent System

Multi-agent systems are required when the task requires coordination among other agentic systems. These systems allow multiple AI Agents to work in tandem by sharing states and data. These systems are useful when tasks are interconnected and the actions of one agent affect others.

  • Example: A collaborative crew of AI agents that consists of a research agent, an analyst agent, and a coding agent. The research agent with access to knowledge bases can autonomously extract relevant information, the analyst agent will analyze the data and instruct the code agent to prepare graphs and plots summarizing the result.

Components of Artificial Intelligence Agent Architecture

The architecture of an AI Agent depends on the specific application and requirements. The architecture can be physical, software-based, or a mix of both. So, let’s discuss the components of an agent system.

  • Sensors/Prompts: The agent receives external stimuli via sensors or text prompts. A physical robotic agent perceives the surroundings via a camera, mic, proximity, RADAR, and other such sensors. The input could come from these sensors or be provided in text format for software-based systems. For example, data can be provided in JSON, XML, or other structured text formats.
  • Actuators/Tools: The actuators and tools help the agent execute tasks in the real world. Robotic systems depend on wheels, hands, legs, etc, while software-based systems use tool integrations.
  • Processors/Decision-making system: These handle inputs from sensors, analyze the data, and determine the appropriate actions to accomplish a given task. Usually, this is an AI model.
  • Knowledge Base: For long-term memory, previous interactions, or any external data is stored in a database. This enables the agents to access external data as and when needed.

Example: A self-driving car

  • Sensors: A self-driving car uses LIDAR, RADAR, and a Camera to perceive its surroundings, and navigate traffic and other obstacles. It may receive voice instructions from passengers through a mic.
  • Actuators: The car uses the steering wheel, brakes, and other mechanical components to drive.
  • Processors: The car’s onboard computer will use an AI model to process input data to avoid obstacles and find optimal routes.
  • Knowledge Base: The car may have databases for storing map data, route information, and other such data to aid in better navigation.

How does AI Agents Work?

So far, you have learned what makes an AI Agent, the types of agents, and the different components of a typical AI Agent system. To summarize, AI Agents are systems that can dynamically interact with their environment with the help of sensors, actuators, AI Models, and Knowledge bases. Now you will learn how these components work together to achieve a goal.

  • Goal Initialization: The first step of the process is to provide the LLM in the back end with the desired goal. The LLM processes the goal and acknowledges the objective.
  • Task Planning: The LLM prepares a step-by-step task list to accomplish the job and starts searching for components to finish jobs.
  • Tool use: The LLMs are provided with a set of tools, and depending on the task, they will pick appropriate tools to accomplish the task. For example, if the task requires gathering information from the web, the LLM will choose a tool to surf the internet and collect data.
  • Data Storing and Accessing: If the data needs to be saved on disk or in a database, the agent will select a tool to store the data in the appropriate format. The agent can also access data systems for task execution. For example, an AI Agent can retrieve documents from a file system to process them for further downstream tasks like report generation.
  • Termination: The workflow ends when predefined conditions are met. This can occur when the execution is complete, or when the agent lacks access to the necessary tools and reaches a threshold number of iterations.

This is the overall structure of typical agentic workflows.

AI Agents Example

Let’s explore some promising real-world examples of AI agents.

Figure’s Humanoid Robots

Figure, a robotics company supported by OpenAI, launched a humanoid robot powered by OpenAI’s multi-modal GPT model. The robot perceives the environment via a camera, mic, and other sensors. When the robot receives the command, it uses the AI model’s reasoning and decision-making ability to understand the task and uses actuators to finish the job. The robot has also shown the capability to learn by seeing activities.

Humanoid rebot created by AI Agent

Devin: The First AI Software Engineer

Devin from the Cognition Labs took the internet by storm when it showcased its remarkable software development skills. It could navigate the GitHub repository, fix codes, and many more. It showed 13.86% accuracy on the SWE bench, a benchmark for AI SWE tasks. After Devin, many open-source alternatives have emerged showing similar or better performance.

Waymo Self-driving Cars

Google’s Waymo has turned the vision of autonomous driving into reality, enabling cars to travel from point A to point B without human intervention. With advanced sensors, AI model, and learning systems, the cars can process their environment to navigate traffic, avoid obstacles, and reach their destination safely.

Similar technologies like Tesla’s FSD and CommaAI’s Openpilot, are revolutionizing self-driving.

Image shows AI Agent Example of self driving car

Applications of AI Agents

AI agents can be utilized across various business sectors, from customer relationship management and sales to personal productivity and software development. Here are some use cases of AI Agents.

1. AI Agent in Customer Relationship Management (CRM)

AI Agents can change the way businesses interact with customers. AI Agents can automate customer support, and personalized interactions, manage and analyze data, assist sales teams, and collect feedback. These agents can respond to customer queries, assist, auto-update customer feedback for trend analysis, and offer real-time sales insights. This can save businesses costs and time and free up personnel to work on more complex and creative activities.

2. Productivity

AI agents can be game changers in the realm of personal productivity. They can automate routine tasks such as scheduling meetings, managing emails, setting reminders, etc. By integrating with various productivity tools, AI agents can manage to-do lists, prioritize tasks by deadlines and importance, and offer personalized suggestions to boost efficiency.

3. HR/Hiring

There are different ways AI agents can improve hiring and other HR processes. They can be used to scan LinkedIn profiles, score the candidate, and put it into Google Sheets. AI agents can also grade or filter resumes based on some pre-defined criteria. They can also be used to collect automated survey responses from employees.

4. Software development

There are agents like Devin and OpenDevin that assist developers by automating code generation, debugging, and even optimizing code. But even on a personal level, you can build agents to aid in improving your productivity. For example, an automated GitHub PR agent that summarizes the diffs in a new PR and tags relevant members from the team for manual review.

Benefits of Using AI Agent

AI agents can provide value at every stage of a business and be incorporated across various business verticals. From efficient hiring and customer service to improved sales and administration integrating AI agents in workflows can drive productivity and profitability.

1. Improved Efficiency

AI agents can handle tedious, repetitive tasks such as data entry, scheduling, and basic analysis. This frees up time and resources for other activities. Companies can allocate resources to more demanding and creative projects by assigning these tasks to AI agents,

2. Enhanced Personalization

AI agents excel at effective personalization by analyzing custom data. Companies can integrate AI agents into their products to deliver tailored experiences. With access to customer data and browsing history, AI agents can offer personalized solutions to customer queries.

3. Higher Availability

In many situations requiring 24/7 availability, AI agents can complement human staff to enhance the overall experience. AI agents can handle simpler tasks and queries, allowing human staff to concentrate on more complex tasks or those that require a human touch.

4. Scalability

AI agents are highly scalable. The agents can be scaled to meet surging demands without requiring additional human resources. The scalability ensures that businesses can continue to deliver quality services even during peak times.

Challenges and Limitations of AI Agents

Despite the numerous benefits of AI agents, the technology is still in its early stages. The infrastructure, frameworks, tooling ecosystem, and protocols are still being researched and developed. Many AI agents currently available in the market are unreliable and lack practical utility. The agents are bloated and less production-friendly. Also, the running cost of AI agents is huge, largely due to running frontier models like GPT 4, and Claude Opus being very expensive. In addition to that, the tooling ecosystem is still very immature for building production-ready AI agents.

Additionally, there is an increasingly negative perception regarding the use of AI agents as they are branded as a replacement for the human workforce. However, in reality, this is the farthest from the truth. As the technology currently stands, AI agents cannot replace humans but can be used to complement human employees, thereby enhancing shareholder value.

We have just discussed the challenges and limitations hindering the wide-scale adoption of AI agents in critical applications with significant consequences. The future endeavors will be about making efficient infrastructure, frameworks, and protocols for developing reliable agents. A big problem with AI agents is the AI models themselves. Current AI models are expensive and cost-intensive. With growing interest, we anticipate more companies developing high-quality models; this subsequently will drive down costs.

How Can Composio Assist with Your AI Agent Needs?

Composio is building the tooling infrastructure for the next-generation AI agents. Composio allows the production-readyl integration of 150+ tools to agents to accomplish more. The tools seamlessly integrate with popular AI agent frameworks like LangChain, CrewAI, and AutoGen, making it easier for AI engineers to build reliable agents.

How to create intelligent agent in ai using Composio

Composio is designed for production environments, offering safe and secure managed authentication, popular app integrations, and user-friendly APIs, allowing you to focus on delivering results rather than reinventing the wheel.

You can seamlessly integrate tools like Slack, Discord, Trello, Asana, GitHub, and many more apps to augment your AI agent workflows. You are not limited to this, Composio also provides the convenience of defining custom tools for your specific needs.

Read this article to learn more about Composio’s tool integrations.

https://composio.dev/blog/ai-agent-tools/ 

Build AI Agents with Composio

With extensive tool integrations, Composio allows you to build reliable AI agents. These tools come with various actions and triggers to achieve specific objectives. Composio enables agents to execute tasks requiring interaction with the external world via APIs, RPCs, Shells, File Managers, and Browsers.

Agents can now execute code, interact with your local system, receive triggers, and perform actions for 150+ external tools.

For instance, to accomplish a task like “Create a new repository on GitHub,” your agent needs to integrate with GitHub’s API. This involves translating API specifications into callable functions, managing authentication for multiple users, and other complexities that Composio handles out of the box.

Gif shows how Composio can Help to develop AI Agents

Composio also provides an interactive dashboard that keeps track of all your authenticated tools.

Image shows guide to explore how to build AI agents with Composio

Check out this in-depth walk-through guide to explore how to build AI agents with Composio.

https://www.analyticsvidhya.com/blog/2024/05/ai-research-assistant-using-crewai-and-composio/

Conclusion

The field of AI is rapidly evolving. While we are still in the early stages of this technological revolution, the advancements made so far are promising. AI agents can handle repetitive tasks, enhance personalization, provide 24/7 availability, and scale effortlessly to meet growing demands. Despite the challenges and limitations, the future looks bright with continuous improvements in AI models and supporting infrastructures.

Composio is a key contributor in this field, offering the essential tools and integrations needed to build robust AI agents. With its production-friendly environment, secure authentication, and extensive toolset, Composio enables businesses to harness the power of AI efficiently and effectively. Companies can enhance productivity, improve customer experiences, and drive innovation, by integrating AI agents into various business processes,

AI agents FAQ

1. Is ChatGPT an AI agent?

ChatGPT is not an AI agent in the traditional sense. However, it shows many agent-like characteristics like input sensors (mic, camera), actuators (tools like web search, Dalle image generation, Code-interpreter), knowledge bases (It can remember messages across chats), and the LLM itself.

2. Are GPTs AI agents?

GPTs (Generative Pre-trained Transformers) themselves are not AI agents. They are language models that generate text based on the input they receive. However, they can be integrated into AI agents to provide natural language understanding and generation capabilities.

3. Are AI agents sentient?

While there are raging debates going on about current AI models having consciousness. It is generally accepted that AI models are not sentient. They operate based on programmed instructions and learned patterns from data.

4. Will AI agents take our jobs?

While AI agents can and will automate some jobs, they are not direct replacements for humans. They are more effective when used as complementary tools rather than substitutes. AI agents tend to fail in complex situations, when that happens you would want human to interfere and get it done.

5. Do AI agents perpetuate bias and discrimination?

Yes, AI agents can perpetuate bias and discrimination. The behaviors of AI agents depend on the data they have been trained on. A biased dataset will result in a biased model.

6. Who’s to blame when an AI agent makes a mistake?

This is a matter of debate and discussion. As the field matures, we can expect proper laws and regulations to be enacted for customer protection. However, it is important to develop ethical guardrails and reliable software systems to mitigate mistakes with huge consequences.

7. What is a goal-based agent?

A goal-based agent is an AI agent designed to achieve specific objectives or goals. It evaluates different actions based on how well they contribute to achieving the goal and can plan and execute sequences of actions to reach the desired outcome.

8. What is a performance element in the context of AI agents?

The performance element in the context of AI agents refers to the component that determines the agent’s actions. It is responsible for selecting the actions that will maximize the agent’s performance based on the information it receives from its sensors and the goals it aims to achieve.

9. How does a language model differ from other AI agents?

A language model, like GPT, is designed to generate and understand text based on patterns learned from large datasets. It does not autonomously perform tasks or interact with its environment. In contrast, AI agents are designed to perform tasks, make decisions, and interact with their environment autonomously.

10. What are reactive agents, and how do they operate?

Reactive agents respond to environmental stimuli based on pre-defined rules. They do not maintain an internal model of the world or plan long-term actions. Instead, they operate by mapping inputs directly to actions, making decisions based solely on current perceptions rather than past experiences or future goals.

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