AI agents are the latest buzzword to hit the rapidly evolving world of AI tools. What are they and why do we care? Let’s start with the second point before delving into the technical details of AI agents. 

AI agents are tools that use AI to respond to their environment and take actions based on what they “perceive.” AI agents present a powerful method for businesses to automate strategy, harnessing AI to identify patterns and respond to changing market conditions more rapidly and more accurately than people can respond—and that’s assuming there’s anyone who could maintain the level of continuous scrutiny that AI agents are built for. AI agents provide the capability to reduce errors and seize opportunities far faster than ever before. 

So how do they work? 

As we’ve already touched on, AI agents are software programs designed to perceive their environment, make decisions, and take actions to achieve specific goals. They use artificial intelligence techniques to process information, learn from experiences, and adapt their behavior. AI agents can range from simple rule-based systems to complex neural networks capable of handling diverse tasks across various domains. 

These agents use AI models to handle information, learn from what happens, and change how they act. They come in many forms, from basic systems that follow simple rules to complex ones that can handle various tasks. 

Role of LLMs in AI agents? 

Large Language Models (LLMs) are crucial in improving AI agents’ work. LLMs act like a brain for many agents, giving them lots of knowledge and the ability to understand and use language. This helps agents talk to people naturally, figure out tricky tasks, and make good decisions based on lots of information. Because LLMs are so flexible, agents can do many different jobs without needing to be reprogrammed entirely each time, making them more useful and more accessible for people to work with. 

How does an AI agent work? 

An AI agent works through a complex interplay of inputs, processing, and interactions with its environment and knowledge resources. 

Here’s a breakdown of its operation:

1. Input Processing: The agent starts with its core abilities, goals, prior knowledge, and past experiences. These form the basis for its decision-making.
2. Environmental Interaction: The agent takes actions in its environment and receives observations in return, creating a continuous feedback loop.
3. LLM Utilization: The agent queries a Large Language Model for additional information or complex reasoning, enhancing its knowledge and decision-making capabilities.
4. Decision Making: Based on all inputs, environmental feedback, and LLM assistance, the agent decides on the following actions.
5. Continuous Learning: This process repeats continuously, allowing the agent to adapt and improve through new experiences and information.

This cyclical process allows the AI agent to operate autonomously, adapt to new situations, and improve its performance over time. 

Benefits of using AI agents 

AI agents automate tasks, boost efficiency, and save costs by handling repetitive work quickly and accurately. This means that in business, AI agents can streamline operations, reduce costs, and enhance productivity by automating routine tasks. They help make data-driven decisions faster, leading to better strategies. Additionally, AI agents improve customer service through personalized experiences, which can lead to higher customer satisfaction and loyalty. 

Some use cases would include: 

1. Customer Support: Automate responses to customer queries through chatbots, providing 24/7 assistance.
2. Data Analysis: Analyze large datasets to identify trends and insights for better decision-making.
3. Sales Automation: Streamline lead generation and follow-up processes, improving sales efficiency.

The main challenge with AI agents 

AI agents require access to large amounts of data, raising concerns about data protection. This means that every business trying to utilize these agents must have a secure mechanism and simultaneously provide data access to the AI and the agent to facilitate learning from experience and enough data for reasoning. 

Current architectures, which require us to upload data to a vendor’s cloud or copy it into a vector database, isn’t going to work well. What we need is real-time infrastructure for AI to solve problems with AI. At SWIRL, we’re solving the problem by providing access-based, federated learning experiences to AI models. In a manner that it can access data per role-based access and doesn’t require data re-upload, making the whole process secure and safe.  

Using AI Agents securely with SWIRL 

SWIRL ensures data privacy and security by securely accessing data without uploading, which is often a significant concern with AI systems. It allows AI models and agents to work with multiple data sources without moving data, preserving confidentiality.  

This also reduces implementation complexity and costs associated with traditional data integration methods like ETL. Furthermore, SWIRL’s secure setup ensures that AI agents can access the correct data with proper permissions, reducing the risk of bias and errors while maintaining compliance with data regulations. To learn more about SWIRL, you can book a call with the team.