Consider this: you finally build your AI system, and users are excited to interact with it. But when they do, it spits out offensive, biased, or just plain wrong responses. You think to yourself, what happened? I thought I built an AI agent. What went wrong?!  It could take months to recover from this AI failure. Not only will you have to fix the problem, but you'll also need to rebuild trust with your users. Trust that you can deliver a reliable, accurate AI system. 

Developing AI agents that function correctly and produce trustworthy results takes a lot of work. Getting there involves creating a solid foundation for your AI system, including proper testing and monitoring. This guide will help you get there. We’ll discuss how to build AI agents and offer valuable insights to help you achieve your goals, like building trustworthy AI systems. 

OpenSesame's AI Agent infrastructure can help you get there faster. It provides a solid framework for building accurate, reliable AI agents that your users can trust. 

What Is An AI Agent?

An AI agent is a computer program that can collect data from its environment and use that data to perform tasks without human intervention. Humans set the goals for AI agents, but the agents independently determine how to achieve those goals. For example, a contact center AI agent might aim to resolve customer queries. To do this, the agent automatically interacts with the customer, asking questions, looking up information in internal databases, and responding with a solution. Based on the customer’s responses, the AI agent determines whether it can resolve the query independently or needs to pass it on to a human. 

What Makes AI Agents Different?

All software autonomously completes different tasks as determined by the software developer. So, what makes AI or intelligent agents unique? 

AI agents are rational agents. They make logical decisions based on their perceptions and data to produce optimal performance and results. An AI agent senses its environment with physical or software interfaces.

For example, a robotic agent collects sensor data and a chatbot inputs customer queries. Then, the AI agent applies the data to make an informed decision. It analyzes the collected data to predict the best outcomes that support predetermined goals. The agent also uses the results to formulate the following action that it should take. For example, self-driving cars navigate around obstacles on the road based on data from multiple sensors.

Applications of AI Agents

AI Agents Transform Healthcare by Enhancing Surgical Precision and Performance 

In the healthcare industry, artificial intelligence agents help assist surgeries with better accuracy, reduce infection risks, and minimize blood loss. It also helps organize and store medical records, simplifying access and enhancing work accuracy.

AI Agents Streamline Education by Automating Administrative Tasks  

AI agents in the education sector help automate administrative tasks like grading, communication management, course administration, and personalized messaging. They also enhance teaching by digitizing methods and content, helping create customized learning experiences.

AI Agents Supercharge eCommerce and Retail to Improve Customer Experience  

AI agents help improve inventory management by predicting demand and ensuring optimal stocking levels for eCommerce and retail businesses. They also enhance customer experience by providing personalized product recommendations and marketing campaigns.

AI Agents Enhance Finance by Boosting Fraud Detection and Customer Service  

In finance, AI agents help detect and prevent fraud by analyzing transaction data to identify risks and take proactive measures. It further enhances customer service by providing instant support, handling queries, and providing personalized financial suggestions, thus improving customer satisfaction.

AI Agents Tackle Manufacturing Challenges to Improve Quality and Efficiency  

AI agent applications are also widely used in the manufacturing sector, where they enhance efficiency and product quality through predictive maintenance. They analyze equipment data to minimize downtime and optimize operations, improving quality control and reducing errors.

AI Agents Transform Marketing to Boost Campaign Performance  

AI agents improve targeting, increase ad inventiveness, and analyze consumer behavior to maximize advertising in marketing by increasing engagement and conversions. By extracting insights from consumer data, they boost customer satisfaction and marketing effectiveness through tailored interactions and effective methods.

AI Agents Improve Recruitment Processes and Promote Diversity  

In recruitment, AI agents enhance candidate selection through blind hiring, using ML algorithms to evaluate candidates without demographic bias, and promoting diversity. Additionally, AI tools efficiently screen and filter resumes based on industry-specific keywords, streamlining recruitment and improving talent acquisition. 

OpenSesame: The Hallucination-Busting AI Agent Platform

OpenSesame offers innovative AI agent infrastructure software that grounds AI models in reality. Our platform reduces hallucinations, enhances reliability, and saves hours of manual checking. Key features include real-time hallucination reports, business data integration, multimodal AI expansion, and open-source frameworks. 

We provide ungrounded truth recognition, prompt template extraction, accuracy scoring, and a hallucination dashboard. OpenSesame allows businesses to confidently build trustworthy AI systems, offering real-time insights without latency for high-performing, reality-grounded AI solutions. Try our AI agent infrastructure management software for free today!

How Are AI Agents Made?

All AI agents have an architecture that serves as their base. Depending on the agent, this architecture can be a physical structure, a software program, or a combination. For example, a robotic AI agent consists of actuators, sensors, motors, and robotic arms. Meanwhile, the architecture that hosts an AI software agent may use a text prompt, API, and databases to enable autonomous operations. 

How Do AI Agents Function? 

An agent function describes how the data collected is translated into actions that support the agent’s objective. When designing the agent function, developers consider the type of information, AI capabilities, knowledge base, feedback mechanism, and other technologies required. 

What Is an AI Agent Program? 

An agent program implements the agent function. It involves developing, training, and deploying the AI agent on the designated architecture. The agent program aligns the agent’s business logic, technical requirements, and performance elements. 

Related Reading

• Trustworthy AI
• AI Problems
• Contextual AI
• AI Decision Making

Types of AI Agents

1. Simple Reflex Agents: The Most Basic AI Agents

Simple reflex agents are among the most basic examples of artificial intelligence. They take action based on the current percept, ignoring the rest of the percept's history. As a result, simple reflex agents operate well only in fully observable environments.  A simple reflex agent does not consider any part of the percept history during decision-making.  It uses a condition-action rule that maps the current state to an action. For example, a room-cleaning agent works only if there is dirt. 

Limitations of Simple Reflex Agents

• Very little intelligence. 

• No knowledge of non-perceptual parts of the current state. 

• Condition-action rules can be too complex to generate and store.

• Not adaptive to changes in the environment.
  
  

Example of a Simple Reflex Agent: Thermostat

A basic thermostat is a simple reflex agent. It senses the room's temperature (the percept) and turns the heater on or off based on a predefined rule: If the room is colder than a set temperature, the heater is turned on, and if the room is warmer, the heater is turned off. 

Limitations

It doesn’t account for humidity or external weather, just the current room temperature.

2. Model-Based Reflex Agents: A Step Up from Basic Agents

Model-based reflex agents are more advanced than simple reflex agents. They can operate in partially observable environments and track the situation as they proceed. A model-based agent has two critical factors: 

Model

Knowledge about "how things happen in the world."

Internal State 

A representation of the current state based on percept history.

These agents have a model, "knowledge of the world," and based on the model, they perform actions. Updating the agent state requires information about the following: 

• How the world evolves. 

• How the agent's action affects the world.
  
  

Example of a Model-Based Reflex Agent: Self-Driving Car

Self-driving cars are model-based agents. They have an internal model of the road, traffic rules, and other vehicles around them. The car uses this model to keep track of what’s happening, even when some information is not directly observable (like when it predicts the path of another vehicle it cannot currently see). 

How it works

The car updates its internal state with data about the surroundings and reacts to obstacles, pedestrians, and changing road conditions.

Challenges

We deal with partial observability, like blind spots or unpredictable human behavior.

3. Goal-Based Agents: Moving Beyond the Basics

Model-based agents can make decisions based on their knowledge of the environment. However, sometimes, more than knowledge of the current state is required. An agent may need to know its goal, which describes a desirable situation. Goal-based agents expand the capabilities of the model-based agent by having the "goal" information. They choose an action to achieve the goal. 

These agents may have to consider a long sequence of possible actions before deciding whether the goal is achieved. Such considerations of different scenarios are called searching and planning, which makes an agent proactive. 

Example of a Goal-Based Agent: Chess-Playing AI (like AlphaZero)

A chess AI has the goal of winning the game. It examines many possible moves and strategies to achieve the goal of checkmating the opponent's king. 

How it works

The AI plans moves by considering various scenarios and selecting actions that bring it closer to its goal (winning).

Challenges 

It must search and evaluate countless potential future moves before deciding.

4. Utility-Based Agents: The Sophisticated Decision Makers

Utility-based agents are similar to goal-based agents but provide an extra component of utility measurement. This feature makes them different by providing a measure of success at a given state. Utility-based agents act based not only on goals but also on the best way to achieve the goal. 

The utility function maps each state to an actual number to check how efficiently each action achieves its goals. Utility-based agents are helpful when there are multiple possible alternatives, and an agent has to choose to perform the best action. 

Example of a Utility-Based Agent: Ride-Hailing App (e.g., Uber’s Surge Pricing)

A ride-hailing app uses utility-based principles when determining pricing. Its goal is to balance rider demand with driver availability, but it goes beyond simply matching rides. It uses a utility function to maximize revenue while ensuring customer satisfaction. 

How it works

The utility function considers factors such as current ride demand, driver availability, and time to destination and sets prices accordingly. 

Challenges

We balance rider satisfaction with profitability in various conditions (peak hours, weather disruptions).

5. Learning Agents: The Self-Improving AI Agents

A learning agent in AI is the type of agent that can learn from its past experiences or it has learning capabilities. It starts to act with essential knowledge and then can act and adapt automatically through learning. A learning agent has mainly four conceptual components: 

Learning element

Responsible for making improvements by learning from the environment. 

Critic

The learning element takes feedback from the critic and describes how well the agent performs against a fixed performance standard. 

Performance element

Responsible for selecting external actions. 

Problem Generator

This component suggests actions that will lead to new and informative experiences. 

Hence, learning agents can learn, analyze performance, and look for new ways to improve performance. 

Example of a Learning Agent: Recommendation Systems (like Netflix or Amazon)

A recommendation system improves over time by learning user preferences. Initially, it may suggest basic recommendations based on general popularity, but over time, it learns from users' actions (e.g., what they click on or rate highly) and refines its suggestions. 

How it works

The system uses a learning element to gather feedback from users' actions (such as what they watched or didn’t finish) and improve its recommendations. 

Challenges

Continuous adaptation to new user behaviors and preferences without overwhelming users with repetitive or irrelevant suggestions.

Can I Build An AI Agent On My Own?

Of course, you can build your own AI agents. These self-operating programs can help you boost efficiency and productivity, reduce costs, enhance user experience, and more. Think of them as virtual assistants who can carry out various tasks on autopilot, taking over functions you’d typically perform manually. The most popular AI agents you’ve probably heard of include chatbots and recommendation systems, but several different types exist. 

The Process of Building an AI Agent

Building an AI model requires high-quality data and can be done through different methods, including no-code/low-code platforms, AutoML, and traditional programming. The choice of method depends on coding expertise, customization needs, and time availability. 

Pinpoint the Specific Problem You Want to Solve

The first step in crafting your AI model is pinpointing the specific problem and understanding how AI can tackle it. Focusing on what's bothering the user and figuring out the model's value proposition can help you shape an AI model that genuinely helps its users. Do you want to analyze customer behavior? Have you automated marketing campaigns? Have you improved customer service? Whatever your business objectives, be clear on how your model will support them. 

Get Your Data in Order

To build any of these models, you must ensure you can gather relevant and timely data. Cleaning and organizing data is a big deal in AI system-building. The data quality used for training, whether structured or unstructured, plays a massive role in how well your AI system performs. The amount of data is necessary, too; you'll need enough for the model to learn the patterns within it thoroughly. 

Cleaning data is like tidying it up before AI model training. You sort it out, chuck out the incomplete bits, and put it to make sense of it. You need to make sure you have the correct data types as well. The aim is to fix or remove errors, ensuring the AI model learns from accurate and reliable information in the training data. 

Create Algorithms to Process Your Data

Once your data is clean, it's time to create algorithms. These are like math instructions telling the computer what to do, how to process data, and how to make predictions. 

Train Your AI Model

Now, it's training time. You feed your data into the algorithms, letting them learn the ropes. They adjust themselves to improve – tweaking parameters and weights for peak performance. Optimizing these algorithms is vital for high accuracy during training. Fine-tune those parameters to change the model setup – the goal is top-notch performance. To ensure your AI model hits the mark, set a minimum acceptable threshold for the performance metric that matters most to you (e.g., accuracy, precision, or recall). This is the level of performance considered good enough for the model.

Deploy Your AI Model

Finally, once your AI model is trained and fine-tuned, it's time to deploy it, evaluate its performance, and monitor it. Monitoring and maintenance are vital to keeping the model performing well. Regular checks allow for any needed tweaks or improvements. Does this sound like a lengthy process? It may be necessary to do it with hand-crafted code, or it can all be handled smoothly with automated tools.

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• Trustworthy AI
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• AI Decision Making

How To Build An AI Agent in 8 Simple Steps

1. Leverage OpenSesame.dev for AI Agent Development

OpenSesame is a platform that provides innovative AI agent infrastructure software to help you build reliable AI models. The software grounds AI in reality to reduce hallucinations and enhance performance. OpenSesame offers features like real-time hallucination reports, data integration, multimodal AI expansion, and open-source frameworks to help you build trustworthy AI systems. Try our AI agent infrastructure management software for free today!

2. Define the Task and Environment for Your AI Agent

Before you start building your AI agent, clearly define its purpose. Identify the environment it will operate in, whether that’s an app, a website, or a CRM system. This will help you ensure the agent is compatible with its surroundings once implemented. Next, determine what tasks you want the AI agent to handle. These will vary depending on the industry.

3. Assemble Your Development Team to Build An AI Agent

Next, you need to build your development team. They will be responsible for gathering the data used to feed your AI agent. The specific programming languages, platforms, and other technologies you’ll use will also depend on their skills and expertise. Here’s who you’ll likely need on your team: 

• Machine Learning Engineer

• Data Scientist

• Software Engineer

• UI/UX Designer

• DevOps Engineer
  
  

4. Gather Data to Train Your AI Agent

Data is the lifeblood of any AI agent. Without quality data, your AI will be like a car without fuel — it simply won't run. The key is to gather relevant, accurate, and abundant data. There are several sources you can consider, including the following: 

Internal data

This includes data collected within your business, such as sales records, customer information, operational data, and financial reports.

External data

You can obtain this data by purchasing datasets, partnering with providers, or utilizing publicly available data.

User-generated data

This includes customer or user data, such as social media posts, product reviews, or website interactions.

For example, if you want the agent to manage patient health records, you should gather relevant medical data. We applied this approach when we recently developed a proof-of-concept (PoC) for a medical records summarization chatbot. 

This AI-driven tool uses natural language processing (NLP) to extract and summarize essential information from medical records. After gathering your data, it's also critical that you preprocess it. This involves handling missing values, detecting outliers, making the data consistent, and more. By rigorously cleaning and preparing data, you lay a strong foundation for your AI agent's performance. 

5. Select Your Tech Stack for Building an AI Agent

There’s no one-size-fits-all tech stack, and yours will depend on your specific goals and the environment where your agent will be deployed. 

Programming language

The programming language is the foundation of your AI agent’s code. In general, you want to choose your programming languages, such as Python and Java, based on the technology that you will use. To be more specific, you can employ the following technologies in your AI agent: 

• Machine Learning: Learns from data to predict and uncover patterns.

• Natural Language Processing: It enables machines to understand and respond to human language.

• Computer Vision: It allows machines to see and understand the visual world.

• Robotic Process Automation: Automates repetitive tasks in digital systems.
  
  

Every AI Agent is Unique

Our experience developing AI-based solutions, mobile apps, and other software products allows us to select the perfect tech stack for your project. We often blend several of them to ensure excellent performance in the AI agents we develop.

6. Design the AI Agent

Work with your team to design the agent. You need to decide on the agent’s build, how you will handle and process data, and consider user experience. 

Agent architecture 

Choosing the exemplary architecture for your AI agent will define how easy it will be to maintain it in the future and how efficiently it can run. There are two general options that you can consider: 

• Modular design. You’ll create multiple parts of your AI agent separately before assembling them into one working piece, which makes maintenance easier.

• Concurrent architecture. Use a concurrent design if you need the agent to run several tasks simultaneously.

If you are making an AI agent that handles conversations with the user, such as an AI chatbot, your chosen architecture might also affect your conversation flows. To learn more about how to build a conversational AI bot, check our guide. For instance, a modular design can offer greater flexibility when iterating on your decision trees or conversation flows, as it allows individual components to be modified without affecting the entire system. Meanwhile, a concurrent design might be better if your AI agent will handle multiple conversations simultaneously. 

Data handling

Define how your agent will get data. For example, you can design a chat interface where a user can enter information for your agent to process. Similarly, determine how the agent will respond. For instance, you can set it to reply to a user or update a spreadsheet based on the processed data. 

User experience

If your AI agent interacts directly with users, design its appearance. Use buttons, colors, and text that reflect your brand. Don’t forget to add accessibility features, like text-to-speech and more. We also recommend including a feedback mechanism in the AI agent. This way, users can freely provide feedback, which you can use to improve the system.

7. Test the AI Agent Before Deployment

Like any complex system, thorough testing is crucial for your AI agent’s success. Testing helps identify glitches, biases, or unexpected behavior in your agent. It also highlights areas where the agent’s interaction with users can be improved. You can perform the following tests on your AI agent: 

• Unit testing, which involves testing individual modules of the agent’s code to ensure they function correctly in isolation

• Integration testing to verify how different parts of the agent work together smoothly

• Functional testing to check the overall functionality of the agent against its intended use cases

• Usability testing, which involves observing real users interacting with the agent and identifying any usability issues

Optionally, perform edge case testing to determine your AI agent's boundaries by feeding it unexpected or extreme inputs.

8. Deploy and Monitor Your AI Agent

The last step is integrating the AI agent with your existing systems and workflows. If it will handle sensitive data, make sure that you implement proper security measures to protect it and prevent unauthorized access. To ensure your AI agent performs at its peak, monitor it regularly. Track key metrics like accuracy, response times, and resource usage to identify performance issues. Alternatively, actively gather user feedback to understand how people interact with the agent and identify areas for improvement.

4 Tips On How To Mitigate Errors in AI Agents Effectively

1. Use OpenSesame.dev for Improving AI Agents  

OpenSesame offers innovative AI agent infrastructure software that grounds AI models in reality. Our platform reduces hallucinations, enhances reliability, and saves hours of manual checking. Key features include real-time hallucination reports, business data integration, multimodal AI expansion, and open-source frameworks. 

We provide ungrounded truth recognition, prompt template extraction, accuracy scoring, and a hallucination dashboard. OpenSesame allows businesses to confidently build trustworthy AI systems, offering real-time insights without latency for high-performing, reality-grounded AI solutions. Try our AI agent infrastructure management software for free today!

2. Human Insight Is Essential for AI Agent Accuracy  

Training an LLM effectively and avoiding AI errors requires much more than good data. It also requires using human subject matter experts (SMEs) to coach the model into providing accurate and contextually appropriate answers. “Like a human, AI can be wrong, and it can also be compelling,” Downes explains. “Any organization implementing a large language model needs humans to analyze the model’s outputs and guide it towards generating more ‘correct’ responses.” 

This could mean, for instance, that a financial services organization looking to introduce a new AI model might have a team that includes regulatory experts to ensure compliance with economic laws, communication professionals to maximize the effectiveness of customer interactions, and legal advisors overseeing ethical and legal considerations. Meanwhile, data scientists and product managers would also play a key role in aligning the AI's functionality with business objectives and customer requirements.

3. Ongoing Maintenance: Another Key to Effective AI Agents  

Because of AI’s fundamental differences compared with standard software, the training doesn’t end once a model has been deployed. Instead, outputs need to be continually monitored and refined so that they continue to match an organization’s objectives. “LLMs can end up with what’s known as ‘model drift’,” Chittenden-Veal explains. 

“This occurs when the model, initially well-tuned to current data, begins to falter as the underlying business dynamics or customer behaviors evolve.” One study reported a 5-to-20 percent drop off in accuracy due to model drift within the first six months of deployment. 

Chittenden-Veal says that organizations should always be mindful of this, which he describes as rare or unusual scenarios, known as ‘edge cases.’ “AI is essentially a statistical machine, and because it operates via statistics, you’re going to get some edge cases the model can’t effectively handle every now and then,” he explains. “You need humans in the loop to catch and handle these exceptions.”

4. Bite-sized Chunks and Outsourcing AI Agent Implementation  

If this sounds daunting, Chittenden-Veal says it shouldn’t necessarily be. While training data and capturing errors across an organization may be time-consuming and expensive, alternatives exist. He explains that implementing AI doesn’t have to be an all-or-nothing thing. “You need to start implementing AI now, but that doesn’t mean you have to do it across your whole organization,” he says. 

“My recommendation is to start with a small use case. Where you know I've got pain points right now, perhaps with your most high-value people.” “Integrating AI is complex. It’s not just about technology but aligning it with your business processes.” This means ensuring that the AI functions technically, aligns smoothly with the organization's operational goals, and enhances existing workflows.

Meanwhile, Scott Downes says that the complexity involved in implementing and maintaining AI systems requires expertise beyond the scope of most organizations. He says that partnering with an experienced AI provider is usually the most effective way to avoid errors. “The right provider will integrate human feedback with AI capabilities to achieve those most reliable and effective outcomes,” he concludes.

10 Real-Life Examples of AI Agents

1. Roomba (Robot Vacuum Cleaner)

The Roomba uses a simple reflex system. It senses dirt on the floor and moves in response to clean it. Its actions are based on immediate sensory inputs (e.g., detecting dirt or hitting an obstacle) without considering past experiences. 

How it works

When it detects dirt, it moves in that direction, and when it hits a wall or obstacle, it turns and changes its path. 

Limitations

It doesn’t store memory of the layout, so that it can clean the same area multiple times unnecessarily.

2. Industrial Robots

Manufacturing robots that work on production lines often use model-based reflex systems. For instance, an automobile factory's robot arm assembles parts using an internal model of how components fit together. It uses sensors to adjust its actions in real time based on the current state of the assembly. 

How it works

The robot uses a model of objects' behavior, including their shape and weight, and responds to changing conditions, such as part misalignment. 

Use case

These robots are highly efficient in partially observable environments, precisely handling tasks like welding or assembling.

3. Google Maps Navigation

Google Maps is a goal-based agent. It helps users reach a destination by selecting the best route to minimize travel time or distance. The agent constantly updates its route suggestions depending on traffic conditions or accidents. 

How it works

The system plans the route based on current conditions and recalculates if the environment changes (e.g., roadblocks, traffic congestion). 

Challenges

I am handling real-time changes and optimizing routes based on unpredictable events.

4. Autonomous Drones (e.g., delivery drones)

Utility-based agents are used in autonomous drones that deliver packages (like Amazon Prime Air). These drones must not only reach their goal (delivering the package) but also optimize the delivery by considering battery life, wind speed, obstacles, and time. 

How it works

The utility function evaluates various factors (weather conditions, shortest paths, battery efficiency) and selects the best action to maximize the success of delivering the package efficiently. 

Use case

Optimizing delivery in dynamic environments with many variables.

5. AlphaGo (DeepMind’s AI)

AlphaGo is an advanced example of a learning agent that learned to play the game Go. Initially, it was trained using human data, but then it played millions of games against itself, learning and improving from each experience. 

How it works

It uses deep reinforcement learning, learning from its actions and the outcomes to adjust its strategy and improve performance. 

Challenges

Go has more possible board configurations than atoms in the universe, making this a highly complex problem to solve with traditional programming methods.

6. Spotify

Spotify's music recommendation system uses learning algorithms to personalize user song suggestions. The system learns and adapts as the user listens to music, skips tracks, or likes songs, constantly refining the recommendations. 

How it works

The agent continuously takes feedback from user interactions and updates its model to offer better suggestions, learning from millions of users' behaviors. 

Challenges

Balancing new music discovery with familiar favorites while avoiding redundancy.

7. Virtual Assistants (like Amazon Alexa, Siri)

Virtual assistants are examples of collaborative agents. They assist users by setting reminders, playing music, controlling smart home devices, or providing weather updates. They often collaborate with other agents or systems (e.g., a smart thermostat or a streaming service). 

How it works

These agents take user inputs (voice commands), process them with natural language understanding, and perform actions, often relying on web services or other connected devices. 

Challenges

Understanding context, managing multi-step tasks, and collaborating with various external systems.

8. Chatbots (like customer support bots)

Chatbots are interactive AI agents companies use for customer service (e.g., on websites like banking portals or e-commerce platforms). They engage with customers, answering queries, troubleshooting problems, or providing product recommendations. 

How it works

These agents use natural language processing to understand and respond to customer inquiries, learning from interactions to improve future responses. 

Use case

We are automating customer service tasks and handling large queries in real-time.

9. Tesla’s Autopilot

Tesla’s Autopilot is an example of an autonomous agent. Based on sensor input, it can navigate, steer, and accelerate a highway vehicle, following the road rules and adapting to traffic conditions. 

How it works

The car gathers data from cameras, radar, and other sensors to create a real-time model of its environment. It then makes decisions based on this model to control the vehicle autonomously. 

Challenges

We deal with complex, unpredictable scenarios like human drivers making sudden lane changes.

10. Stock Trading Algorithms (e.g., High-Frequency Trading)

Stock trading platforms use multi-agent systems where numerous AI agents work together, each optimizing for different market strategies. These agents buy and sell stocks autonomously based on real-time data, trends, and predictive analytics.

How it works

Each agent executes different trading strategies (e.g., trend-following, arbitrage) and collaborates or competes with other agents to maximize profitability. 

Challenges

They react to market volatility, handle large amounts of data in real-time, and coordinate actions between multiple agents.

OpenSesame: The Hallucination-Busting AI Agent Platform

OpenSesame offers innovative AI agent infrastructure software that grounds AI models in reality. Our platform reduces hallucinations, enhances reliability, and saves hours of manual checking. Key features include real-time hallucination reports, business data integration, multimodal AI expansion, and open-source frameworks. 

We provide ungrounded truth recognition, prompt template extraction, accuracy scoring, and a hallucination dashboard. OpenSesame allows businesses to confidently build trustworthy AI systems, offering real-time insights without latency for high-performing, reality-grounded AI solutions. Try our AI agent infrastructure management software for free today!

6 Incredible Benefits of AI Agents

1. AI Agents: Equipping Efficiency and Productivity in Business Operations 

AI agents excel at handling repetitive and routine tasks, which traditionally consume a significant amount of human resources and time. These tasks include data entry, scheduling, customer inquiries, and fundamental analysis. By automating these tasks, businesses can reallocate their human resources to more strategic and creative endeavors, enhancing overall productivity and innovation.

2. AI Agents: Delivering Tailored Customer Experiences 

One of the standout features of AI agents is their ability to provide personalized experiences to customers. AI agents can tailor recommendations, responses, and services to individual needs by analyzing customer data, preferences, and past interactions. This level of personalization not only enhances customer satisfaction but also drives loyalty and repeat business, as customers feel understood and valued.

3. AI Agents: The Scalable Solution to Business Challenges 

AI agents are inherently scalable. They can handle an increasing volume of tasks or interactions without the need for proportional increases in resources or infrastructure. This scalability is particularly beneficial during peak business periods, product launches, or market expansions, where resource demand can spike dramatically. 

4. AI Agents: Available Whenever You Need Them 

Unlike human employees, AI agents can operate around the clock without breaks, fatigue, or downtime. This 24/7 availability ensures that businesses can provide continuous service, support, or monitoring, which is crucial in today’s fast-paced market. The constant presence of AI agents means that customer queries can be addressed promptly at any time, improving customer experience and satisfaction.

5. AI Agents: Driving Down Business Costs 

Implementing AI agents can lead to significant cost savings. Businesses can save on salaries, training, and related expenses by reducing the need for a large workforce to manage routine tasks. Additionally, AI agents can help optimize processes and identify efficiencies, reducing operational costs over time.

6. AI Agents: Delivering Valuable Data Insights 

Modern-day AI agents can efficiently gather and process large volumes of data. As a result, businesses that use AI agents can gain valuable insights into customer behavior, market trends, and operational efficiencies. These insights can help companies make more informed decisions, tailor their strategies, and stay ahead of the competition.

Try Our AI Agent Infrastructure Management Software for Free Today

OpenSesame offers AI agent infrastructure software that reduces hallucinations in AI outputs. Built for business, the platform grounds AI models in reality to enhance reliability, save time on manual checking, and ultimately help companies build trustworthy AI systems. 

How OpenSesame Reduces Hallucinations in AI Outputs

OpenSesame provides tools and features that promote accuracy in AI outputs to help users identify and fix hallucinations, which are false or misleading information generated by AI. The platform’s key features include: 

Real-time Hallucination Reports

OpenSesame provides immediate feedback on hallucinations as users interact with AI systems to create business outputs.

Business Data Integration

The platform grounds AI models in reality by integrating existing business data, which helps ensure that generated outputs are accurate and relevant to the organization’s operations.

Multimodal AI Expansion

OpenSesame supports multimodal AI, which generates outputs across different formats (text, images, code, etc.) to enhance business operations. The platform helps reduce hallucinations in multimodal AI systems to promote accuracy across all output types.

Open-Source Frameworks

The platform is built on open-source frameworks to enhance customization and foster a community of users contributing to the software's development. 

Features That Promote Grounded AI Outputs

In addition to the key features mentioned above, OpenSesame includes several tools that specifically target hallucinations in AI outputs. These features include: 

Hallucination Dashboard

The dashboard provides a visual representation of accuracy scores and prompts users on how to improve AI outputs with real business data.

Ungrounded Truth Recognition

This feature identifies ungrounded truths in AI outputs so users can recognize and fix hallucinations before deploying business solutions.

Prompt Template Extraction

OpenSesame can extract prompt templates from AI outputs to help users understand how to retrain AI models for more accurate results.
 

Accuracy Scoring

The platform scores AI outputs to provide users with measurable performance metrics and improvement areas. 

OpenSesame allows businesses to build trustworthy AI systems with confidence. It offers real-time insights without latency for high-performing, reality-grounded AI solutions. Try our AI agent infrastructure management software for free today!

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