They’re powered by Large Language Models (LLMs), which are advanced AI systems trained to understand and generate human language.But how do these AI agents actually work, and what does their rise mean for our daily lives and workplaces? Let’s break down the technology, its impact.

The ReAct Framework

ReAct (short for Reasoning and Acting) is a method that helps AI agents think through problems and take actions step by step. It solves a key issue with language models: they can explain what to do, but can’t actually do it. For example, an AI might know how to order a product but not be able to place the order itself.
ReAct uses a loop: Thought (the agent thinks about the situation and plans what to do next.) → Action (using an API or searching a website.) → Observation (looks at the results and uses that new information to keep going until the task is done)

AI agents use the Chain-of-Thought (CoT) technique to think more clearly by breaking down complex problems into smaller, manageable steps rather than jumping straight to the final answer. This method works well because it lets the AI focus on one part of a problem at a time. As the agent writes out its reasoning step by step, it reduces mistakes and makes its thinking process more transparent and easier to follow.
So if you ask an AI agent, for example,  to “organize a team meeting next week,” it will check everyone’s availability, suggest times, send invitations, and even follow up with reminders—all automatically.

Types of Memory in AI Agents

AI agents use memory systems similar to how humans remember things. These systems help them stay on track, learn from experience, and improve over time.

• Short-term memory works like a notepad. It stores recent conversations, tool use, and tasks to keep things running smoothly in the moment.
• Long-term memory stores important lessons from past experiences in external databases. This helps agents remember what worked before, avoid mistakes, and improve over time.
• Entity memory keeps track of people, places, and ideas the agent has encountered. This helps the agent understand relationships and recall useful details.
• Contextual memory combines all the above types to maintain a clear, coherent understanding across long conversations or complex tasks.

There are still problems with AI memory systems. Different types of memory don’t always work well together, which can cause confusion or wrong answers.
Another issue is the infinite subtask loop agents sometimes get stuck trying the same failed solution over and over. Some systems fix this by counting failures, but better memory systems could help agents recognize patterns and change their approach more effectively.

Tool Integration: Extending Agent Capabilities

AI agents use different types of tools to go beyond their built-in abilities. Unauthenticated tools are easy to use and don’t need any login or special access. Examples include calculators, weather checks, or unit converters.
Authenticated tools are more powerful but require secure access. These tools often require sophisticated authentication mechanisms and security considerations, particularly in enterprise environments where sensitive data and critical business processes are involved.
First-party tools are internal systems, like company databases or services only used inside an organization.
Third-party tools are external services requiring complex authentication protocols, API management, and often user-specific permissions for services like cloud storage, communication platforms, and specialized software solutions.

Some agents can chain tools together, using the result from one tool as input for the next — like following steps in a recipe. To do this well, they need to handle errors, manage progress, and keep track of the state of each step.

What to expect?

The future of AI agents is rapidly moving toward systems that are not only more autonomous and specialized but also highly collaborative, making them essential in business operations, scientific research, and everyday life. As these agents evolve from their foundational language model capabilities to executing real-world actions, understanding their workings becomes crucial for anyone looking to navigate the changing landscape of artificial intelligence and leverage its benefits for society.