LLM Powered Agents: The Next Frontier in AI

With the rise of Generative AI and Large Language Models (LLMs), the term ‘Agents’ has emerged as a central topic in AI discussions—from technical forums to corporate boardrooms. These systems are now seen as the next frontier of AI innovation, capturing significant attention and excitement. But what exactly are agents, and why is everyone so excited about them?

In this article, I’ll introduce agents, look at what makes them so transformative and explore their challenges and future potential. I have also written a toy example to show how a basic agent can be built using LangGraph, for which the link can be found in implementation section.

Let’s dive into the future of intelligent assistance!

What is an Agent?

At its core, an agent is a system that perceives its environment, processes information, makes decisions, and takes actions to achieve specific goals. Traditionally, agents—powered by rule-based systems, expert systems, and reinforcement learning—have excelled in environments with well-defined rules, such as industrial automation, game AI, and robotics.

However, the integration of large language models (LLMs) has transformed agents, unlocking remarkable new potential. This has expanded the capabilities of agents by enhancing their ability to process natural language, interpret complex instructions, and interact in more human-like ways. By leveraging LLMs, modern agents exhibit greater flexibility and versatility, enabling them to operate effectively and using language as the interface.

And it doesn't stop there. A number of agents, each designed for achieving a specific goal, can collaborate together to achieve complex overall goals in what is termed as Agentic AI.

Traditional AI models Vs RAG Systems Vs Agents

Traditional Models are reactive systems where they respond to user queries based on the data they were trained on. While RAG systems improve on the accuracy and recency of responses provided, they still are reactive and static in nature.

On the other hand, agents are proactive and dynamic with an ability to reason, make decisions and take actions to achieve a specific set of goals.

A Simple Analogy

Note: I have extended this analogy from my previous article on RAG.

Imagine you’re dining at a restaurant with a friend who used to be a regular there but hasn't visited in over a year.

 Scenario 1 (Traditional LLM Approach):

Your friend confidently suggests dishes based on what they remember from past visits. However, when you place the order, you’re informed that many of those dishes are no longer available. This is similar to how a traditional language model works: it provides answers based solely on its training data, which might be outdated or missing recent updates.

Scenario 2 (RAG Approach):

Before making recommendations, your friend takes a moment to review the current menu. They notice several new dishes and changes, allowing them to suggest options that are up-to-date. This is akin to how Retrieval-Augmented Generation (RAG) works: it augments the model’s pre-existing knowledge by retrieving the latest, relevant information in real-time, leading to more accurate and relevant responses.

Scenario 3 (Agentic Approach):

Your friend doesn’t just stop at reviewing the menu. With a goal to create a perfect dining experience, your friend combines his/her knowledge of your food preferences with information about any food allergies or health issues that you are having on that day, talks to the chef about customizations, and even arranges for your favorite dish from a nearby restaurant if it’s unavailable here. He/She handles every aspect of the dining experience, anticipating your needs and acting on your behalf to create the perfect evening. This is an agent in action—dynamic, autonomous, and purpose-driven, going beyond delivering information to actively solving problems and achieving goals for you.

Anatomy of an Agent

A typical agent consists of several components that work together to enable reasoning, decision-making, and action:

Language Model (LLM)

An LLM is like the brain of the agent and serves as the agent’s reasoning and decision making engine. It interprets the inputs, supports decision making based on patterns learned during training, decides on the action to take and generates human like responses.

Planner

The planner is responsible for breaking down the tasks and deciding on optimal sequence of action to achieve the goals. It works in conjunction with LLMs. In certain scenarios, you don’t need an external planner and the role can be played by LLM.

Action/Tool Executor

This component translates the agents output into concrete actions by integrating with tools, APIs and external systems. For example, an agent may access web for a part of the problem and may get context from vector database for another part of the problem.

Memory

The memory module stores relevant information, past interactions, and states thereby ensuring continuity and context awareness. An agent can have two type of memory:

Short-term Memory

This is the log of the queries, thoughts, actions etc. for the current tasks that the agent is doing.

Long-term Memory

This is the log of the queries, thoughts, actions etc. for the tasks that the agent has done over last few weeks or may be months. This enables agent to use to long term context while performing a task.

Feedback Loop

The feedback loop enables the agent to continuously improve its performance by evaluating the outcomes of its actions and refining its approach, if needed. The feedback loop monitors the agent’s actions and outcomes, leveraging mechanisms like automated error detection, Reinforcement Learning from Human Feedback (RLHF), reflection, self-critique etc.

Agency and Reliability in AI Agents

Design of agents faces a trade-off between agency and reliability. The agency in AI agents refers to their degree of autonomy of an agent while the reliability refers to how much an agent’s output is in line with the expectations.

As we increase the level of agency, the reliability of system may suffer and vice-versa. The goal is to balance the two for a given use case. This is determined by how much control is given to the LLM to reason and act, what kind of guard-rails are set and how much of the control is retained in the code.

Examples of trade-off for an e-learning platform

High Reliability: The agent provides K-12 students with answers to curriculum-aligned questions using verified sources. Responses are tightly curated to avoid misinformation.

High Agency: The agent engages students in exploratory learning by answering open-ended questions creatively. While this promotes curiosity, it might introduce inaccuracies or non-age-appropriate content.

Implementing an Agent

One of the prominent frameworks for building LLM-based agents is ReAct (Reasoning + Acting), which combines the reasoning capabilities of Large Language Models (LLMs) with actionable steps in an iterative process. The ReAct framework enables agents to generate intermediate reasoning steps, take actions (e.g., API calls or database queries), and reflect on the outcomes before proceeding further. This framework is particularly effective for tasks that require dynamic interaction with external environments and multi-step reasoning.

I have created a toy example to demonstrate the implementation of a very basic RAG system using OpenAI APIs, LangGraph and Tavily which can be accessed here - Basic Agent Implementation.

Challenges of Agents

The adoption of AI agents does not come without significant challenges. Ensuring accuracy and reliability is critical, especially in high-stakes applications like healthcare. High-agency agents raise concerns about loss of control, as their autonomy may make them harder to monitor and debug. Ethical considerations, such as bias in decision-making, misuse of technology, and job displacement, are pressing issues. Additionally, the complexity of designing and deploying robust, secure agents requires advanced infrastructure, extensive training data, and constant updates to align with evolving requirements.

It is crucial that these challenges are balanced with the benefits that the agents provide to unlock the full potential of AI agents responsibly.

Future Potential of Agents

LLM-based agents have the potential to revolutionize work and daily life by acting as intelligent collaborators and autonomous problem-solvers across diverse domains.

In personal life, AI agents may become like personal life-coaches on demand, adapting seamlessly to your unique needs. They will take care of your busy schedules, remind you of important tasks, and even help you achieve goals like staying fit or managing finances. These agents may become compassionate companions, offering emotional support and easing the lives, making them invaluable in fostering well-being and connection.

They will fundamentally reshape the future of the workforce by becoming versatile collaborators that transcend traditional job roles. These agents will not merely automate repetitive tasks but will actively contribute to creative problem-solving, strategic decision-making, and dynamic team interactions. Imagine a future where agents brainstorm alongside marketers, assist scientists in real-time data analysis, or dynamically code with engineers, creating a workforce that is more agile, innovative, and inclusive. This transformation won’t just optimize efficiency—it will redefine how we think about work paving the way for a profoundly collaborative and innovative era.