AI AGENTS

What are Agents?

AGENT: Adaptive Generative Entity for Networked Tasks This expansion better captures the essence of modern AI agents:
Adaptive: Highlights the agent's ability to learn and adjust its behavior based on new information and experiences.

Generative: Emphasizes the agent's capability to create new content, ideas, or solutions, not just process existing information.

Entity: Represents the agent as a distinct, autonomous unit with its own "personality" and capabilities.

Networked: Reflects the interconnected nature of AI agents, their ability to work with other systems, and their reliance on vast networks of data.

Tasks: Focuses on the purpose-driven nature of AI agents, designed to accomplish specific goals or functions.

Responsible AI

Vidur, inspired by the character from the Mahabharata. Vidur embodies the principles of Dharma and is considered the most knowledgeable person after Lord Krishna, particularly regarding a responsible society.

Autonomous agents are a big step forward in AI, offering the potential to boost productivity, improve decision-making, and solve problems that traditional software couldn’t handle. These agents use the power of large language models (LLMs) along with advanced planning, execution, and the ability to adapt to changing environments.

An agent is a software tool that performs tasks on its own for users or other programs. Unlike traditional software, which needs clear instructions for every action, agents can make their own decisions and act based on their understanding of the situation and their goals. This independence allows agents to manage complex tasks that involve multiple steps, different conditions, and interaction with other systems or people.

Agents Vs Machine Learning/ Deep Learning

Traditional machine learning (ML) systems are designed to handle specific tasks like image recognition, language translation, or predictive analytics. These systems are trained on large datasets to identify patterns and make predictions. While powerful, ML models usually operate within predefined limits and lack the flexibility to adapt to new, unexpected situations without additional retraining.
In contrast, agents take the capabilities of traditional ML even further by adding decision-making and planning skills. They build on ML models, particularly large language models, but go beyond making static predictions. Agents can interpret context, plan actions, execute tasks (also known as "skills"), and respond to changes in real-time. This flexibility allows them to handle more dynamic and complex environments, making them ideal for situations where adaptability and independence are key.
What are the Skills?
In the realm of artificial intelligence, skills serve as the building blocks that elevate an agent's capabilities. These modular units of functionality act as extensions, broadening the scope of what an AI system can accomplish. Let's delve into the nature of these skills and their significance in AI development.

Defining AI Skills

Skills, in essence, are discrete components that augment an AI agent's ability to process information and perform tasks. They can be likened to specialized tools in a craftsman's toolkit, each designed for a specific purpose but collectively enabling a wide range of operations.

These skills come in various forms:

API Calls: These skills allow AI agents to interface with external services, retrieving data or initiating actions beyond their native environment. For instance, an AI might use an API call to fetch real-time weather data or to post updates on social media platforms.

LLM Plugins: Large Language Models (LLMs) can be enhanced with plugins that extend their inherent language processing capabilities. These plugins might enable an LLM to perform specialized tasks like code generation, data analysis, or even creative writing in specific styles.

Custom Functions: Developers often create bespoke functions tailored to specific needs. These can range from simple utility operations to complex algorithms that process data in unique ways.
By integrating skills, we significantly expand an AI agent's repertoire. This expansion has several key benefits:

Enhanced Decision-Making: With a broader set of tools at its disposal, an AI can make more informed and nuanced decisions.

Task Versatility: Skills enable AI agents to tackle a wider array of tasks, making them more versatile and valuable in various applications.

Improved Responsiveness: By leveraging skills, AI can provide more accurate and relevant responses to user prompts.

Implementing Skills in AI Systems

The process of adding skills to an AI system requires careful consideration. Developers must ensure that each skill integrates seamlessly with the AI's core functionality and aligns with its intended purpose. Moreover, the selection of skills should be strategic, focusing on those that provide the most significant enhancements to the AI's capabilities. As AI technology continues to evolve, the development and integration of new skills will play a crucial role in pushing the boundaries of what these systems can achieve. The future of AI lies not just in improving core algorithms, but in expanding the breadth and depth of skills available to these intelligent agents.

In my view, a person with a strong memory has the potential to achieve extraordinary things. Similarly, AI agents also require memory to handle complex tasks more efficiently, maintain state, and share session data. Just to be clear, I’m not talking about RAM here—I’m referring to the ability to dynamically incorporate user inputs, outputs from previous tasks, or past results into the AI’s context. This is crucial for operating within the LLM’s "context window," which is essentially the limit on the number of tokens or content the model can process at once.

Speaking of context windows, Google’s Gemini Pro 1.5 can reportedly handle up to 2 million tokens, which raises the question: Is RAG (Retrieval-Augmented Generation) still relevant? My answer is a resounding yes. RAG offers significant advantages, particularly in terms of security. With RAG, data is stored on-premises, and only relevant content is sent to the LLM along with the prompt, enabling it to generate a response based on the query or question.
While it’s true that you could theoretically upload all your data to a model like Google Gemini and ask your questions there, this approach has a major downside: you’re essentially crossing security boundaries by uploading all your sensitive data to a third-party server. This makes RAG indispensable, as it ensures better security and control over your data. In my opinion, RAG is here to stay.